Audience Atmospherics Monitoring Platform Methods

ABSTRACT

The AUDIENCE ATMOSPHERICS MONITORING PLATFORM METHODS (“ATMOS”) transforms audience atmospherics data via ATMOS components, into TV audience viewing data and ad effects data. A method is disclosed, comprising: receiving, from the user mobile device, an atmospherics data package indicating user instant activity status; obtaining an atmospherics data artifact from the atmospherics data package; extracting user instant activities information based on analysis of the atmospherics data artifact; generating a user viewing status indication based on the user instant activities information; and incorporating the user viewing status indication into viewer measurement data of a user selected channel.

RELATED APPLICATION

Applicant hereby claims priority under 35 USC §119 for U.S. provisionalpatent application Ser. No. 61/504,913 (attorney docket no.21261-002PV2), filed Jul. 6, 2011, entitled “Mobile Remote Media ControlPlatform Apparatuses, Methods And Systems.”

The instant application is related to PCT international application no.PCT/IL2010/000918, publication no. WO/2011/055365, filed Nov. 7, 2010,entitled “System And Method For Mobile Computing Transmission On ANetwork Of Data Associated With A Television Display.”

The instant application is further related to U.S. application Ser. No.______ (attorney docket no. 21261-002US1), filed Dec. 30, 2011, entitled“Mobile Remote Media Control Platform Methods”; U.S. application Ser.No. ______ (attorney docket no. 21261-002US2), filed Dec. 30, 2011,entitled “Mobile Remote Media Control Platform Apparatuses and Systems”;U.S. application Ser. No. ______ (attorney docket no. 21261-003US2),filed Dec. 30, 2011, entitled “Audience Atmospherics Monitoring PlatformApparatuses and Systems”; U.S. application Ser. No. ______ (attorneydocket no. 21261-004US1), filed Dec. 30, 2011, entitled “Media ContentBased Advertising Survey Platform Methods”; U.S. application Ser. No.______ (attorney docket no. 21261-004US2), filed Dec. 30, 2011, entitled“Media Content Based Advertising Survey Platform Apparatuses andSystems”; U.S. application Ser. No. ______ (attorney docket no.21261-005US1), filed Dec. 30, 2011, entitled “Media Content SynchronizedAdvertising Platform Methods”; U.S. application Ser. No. ______(attorney docket no. 21261-005US2), filed Dec. 30, 2011, entitled “MediaContent Synchronized Advertising Platform Apparatuses and Systems”; U.S.application Ser. No. ______ (attorney docket no. 21261-006US1), filedDec. 30, 2011, entitled “Social Content Monitoring Platform Methods”;U.S. application Ser. No. ______ (attorney docket no. 21261-006US2),filed Dec. 30, 2011, entitled “Social Content Monitoring PlatformApparatuses and Systems”; U.S. application Ser. No. ______ (attorneydocket no. 21261-007US1), filed Dec. 30, 2011, entitled “User ImpressionMedia Analytics Platform Methods”; U.S. application Ser. No. ______(attorney docket no. 21261-007US2), filed Dec. 30, 2011, entitled “UserImpression Media Analytics Platform Apparatuses and Systems”; U.S.application Ser. No. ______ (attorney docket no. 21261-008US1), filedDec. 30, 2011, entitled “Mobile Content Tracking Platform Methods”; andU.S. application Ser. No. ______ (attorney docket no. 21261-008US2),filed Dec. 30, 2011, entitled “Mobile Content Tracking PlatformApparatuses and Systems.”

The entire contents of the aforementioned applications are hereinexpressly incorporated by reference.

This application for letters patent disclosure document describesinventive aspects directed at various novel innovations (hereinafter“disclosure”) and contains material that is subject to copyright, maskwork, and/or other intellectual property protection. The respectiveowners of such intellectual property have no objection to the facsimilereproduction of the disclosure by anyone as it appears in publishedPatent Office file/records, but otherwise reserve all rights.

FIELD

The present innovations are directed generally to media control, andmore particularly, to AUDIENCE ATMOSPHERICS MONITORING PLATFORM METHODS.

BACKGROUND

A home TV user may view TV programs from a plurality of channels. Theuser may operate a handheld remote TV controller sold with the TV set toselect TV channels. For example, the user may push buttons on the remotecontroller to switch channels, turn up/down audio volume, power on/offthe TV. Merchants advertise their products to attract consumers. Thusthe TV audiences may interact with the TV and select a desired channelwithout physically touching it via operating the remote TV controller.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying appendices and/or drawings illustrate variousnon-limiting, example, innovative aspects in accordance with the presentdescriptions:

FIGS. 1A-1E show block diagrams illustrating example embodiments ofATMOS;

FIGS. 2A-2B provide data flow diagrams illustrating data flows of TVremote monitoring within embodiments of ATMOS;

FIGS. 2C-2I provide logic flow diagrams illustrating logic flows of TVremote monitoring within embodiments of ATMOS;

FIG. 3A provides a data flow diagram illustrating data flows of mediacontent based advertising within embodiments of ATMOS;

FIGS. 3B-3E provide logic flow diagrams illustrating media content basedadvertising within embodiments of ATMOS;

FIG. 4A provides a block diagram illustrating a client mobile componentwithin embodiments of ATMOS;

FIG. 4B provides a combined data flow and logic flow diagramillustrating client-server interaction within embodiments of ATMOS;

FIG. 4C provides a block diagram illustrating ATMOS table top unitinfrastructure within embodiments of ATMOS;

FIG. 5A provides a data flow diagram illustrating data downloading fromsocial media within embodiments of ATMOS;

FIG. 5B provides a block diagram illustrating infrastructure of a mediameasurement portal within embodiments of ATMOS;

FIGS. 5C-5D provide logic flow diagrams illustrating obtaining socialmedia content within embodiments of ATMOS;

FIGS. 5E-5F provide example data records illustrating data structures ofsocial media data within embodiments of ATMOS;

FIGS. 6A-6F provide logic flow diagrams illustrating social mediacontent taxonomy within embodiments of ATMOS;

FIGS. 7A-7F provide example data flow and logic flow diagramsillustrating cross-channel data collection of media analytics withinembodiments of ATMOS;

FIGS. 8A-8K provide screen shots illustrating user interfaces of amobile client component within embodiments of ATMOS;

FIGS. 9A-9E provide example screen shots illustrating user interfaces ofmedia analytics within embodiments of ATMOS;

FIGS. 10A-10H provide example block diagrams and exemplary screen shotsillustrating cross-channel media analytics within embodiments of ATMOS;and

FIG. 11 shows a block diagram illustrating embodiments of a ATMOScontroller;

The leading number of each reference number within the drawingsindicates the figure in which that reference number is introduced and/ordetailed. As such, a detailed discussion of reference number 101 wouldbe found and/or introduced in FIG. 1. Reference number 201 is introducedin FIG. 2, etc.

DETAILED DESCRIPTION

The AUDIENCE ATMOSPHERICS MONITORING PLATFORM METHODS provides aclient-server interactive platform whereby a user may operate a generalpurpose personal mobile device (e.g., a smart phone, etc.) to receive alist of TV programs schedules and submit a selection of TV channel viathe personal mobile device. In one implementation, the user may operatethe personal mobile device as a TV remote controller. In oneimplementation, the ATMOS may receive the user's selection of a channeland determine what media contents the user has elected to watch. In oneimplementation, the user's channel selection and viewing status may bepopulated to a social media platform, and the ATMOS may obtain userresponse with regard to a TV program from the social media to performanalytics for TV program feedback review.

For example, in one implementation, a user may plug a ATMOS accessory(e.g., 120 in FIG. 1) into his mobile device, such as a smartphone(e.g., an Apple iPhone, BlackBerry, Google Android, Palm, HTC Evo,Samsung Galaxy, etc.), laptop, personal digital assistant (PDA), tabletcomputer (e.g., Apple iTouch, iPad, etc.), and/or the like, tofacilitate communication between the mobile device and a home TV set. Inan alternative implementation, the ATMOS accessory may be a standalonetable top unit which may not need to be attached to a user mobiledevice. For example, the table top unit may communicate with a desktopcomputer, a laptop computer, a cell phone or mobile device and/or thelike via wired or wireless connection (e.g., Bluetooth, WiFi, etc.). Infurther implementations, the table top unit may monitor audienceactivities as further illustrated in FIGS. 1C, 2E-2H. Furtherimplementations of the table top unit are illustrated in FIG. 4C. Withinimplementations, a ATMOS server may obtain real-time TV program listing,including the TV program schedule, advertisement schedule, and/or thelike, from a TV network. The user may then obtain the list of TV programschedules from the ATMOS, e.g., as shown at 115 in FIG. 1. The user maythen submit a channel selection, e.g., tap on the touch screen of themobile device as shown at 105 in FIG. 1. In one implementation, uponreceiving the user channel selection, the ATMOS server may retrieve datarecord from a media content database and check program table todetermine what's the TV program on air. For example, if the user selectsthe channel “CBS,” the ATMOS may ascertain “CBS” has “The Big BangTheory” on air based on the timestamp when the user submits theselection. In one implementation, the ATMOS may further retrieve aprogram table to obtain information with regard to the advertisementstreamed during the intervals of the TV play “The Big Bang Theory”and/or the product placement advertisements tagged in the TV play “TheBig Bang Theory” on “CBS.”

In another implementation, the ATMOS may parse commercial ad informationretrieved at and generate prompt questions, surveys, and/or the like408. For example, if the ATMOS determines the user is supposed to watcha series of “Audi” commercial during the show “The Big Bang Theory,” theATMOS may prompt a survey including questions with regard to automobilepurchasing. In another implementation, the ATMOS may keep a record ofadvertisements that has played on channels the user has recentlyselected (e.g., for a period of past 2 weeks, etc.), and generate promptquestions based on such advertisements. In one implementation, the usermay submit responses to such questions.

In a further implementation, when a user is provided a question withregard to an embedded advertisement in the TV show (e.g., “are youinterested in the red bag the character is carrying?” etc.), the usermay submit a request to learn more and/or purchase the product. In thatcase, the ATMOS may provide a merchant URL to the user and/or redirectthe user to the merchant site.

In one implementation, ATMOS may monitor whether the user is “actually”attending and watching the selected TV channel. For example, the user'smobile device may capture, aggregate and packetize atmospherics data(e.g., taking photos of the user, recoding audio clips, obtaining GPSinformation, etc.) and submit to the ATMOS, which may in turn decode theatmospherics data to analyze ad effect and audience perception, asfurther illustrated in FIGS. 2E-2G.

ATMOS

FIG. 1A shows a block diagram flow chart illustrating work flows ofATMOS within embodiments of the ATMOS. Within embodiments, the ATMOSplatform 105 may facilitate clients, such as merchants, brand namemanufacturers, media producers, and/or the like to plan 105 aadvertisement campaign program, track advertisement targets to determinead delivery 105 c (e.g., whether the ads are viewed by the audience,etc.) and performance 105 b (e.g., whether the advertisements meet asales/brand image goal, etc.).

Within implementations, the ATMOS platform 105 may interact with usermobile devices, e.g., PDAs, smart phones, etc., for targeted mobileadvertisement delivery 104. For example, in one implementation, ATMOSplatform may obtain a TV viewing status information from a user's mobiledevice, and determine the TV program content the user is/has beenwatching based on TV schedules, as further discussed in FIG. 1B. WhenATMOS determines the user selected channel contains a TV ad of “Geico”104 b, ATMOS may deliver promotions, rewards, coupons, questionnaires,etc., related to “Geico” as a targeted ad 104 a to the user mobiledevice.

In one implementation, ATMOS platform 105 may obtain data related touser interactive activities with regard to mobile ads, TV viewing,Internet 102 (including online browsing, purchasing, etc.), socialmedia, and/or the like to analyze ad effects, TV rating so that todetermine delivery 105 c and performance 105 b of an advertisementcampaign. Within implementations, the ad campaign planning 105 a, adperformance 105 b and ad delivery 105 c, may be separately executed bythe ATMOS platform 105 for each media type, e.g., TV, print, Internet,social media, etc.

FIG. 1B shows a block diagram illustrating a user engaging a personalmobile device as a TV remote control within embodiments of ATMOS. In oneembodiment, a user may download a ATMOS client component forinstantiation on his general purpose personal mobile device 115. Forexample, the user may obtain a ATMOS application from the iTunes Storeand download it to his Apple® iPhone, iTouch, iPad, and/or the like. Inone implementation, the user may plug-in a ATMOS accessory 120 to themobile device 115 so that the mobile device may communicate with a homeTV set 130 as a remote controller.

In one implementation, upon instantiating the downloaded ATMOS clientcomponent, the user may receive a schedule listing of TV programs, andmay select a channel that the user is interested. For example, the usermay tap on the listed item to select “CH2: CBS The Big Bang Theory 105.”In one implementation, the ATMOS plug-in accessory 120 may receive theindication of user channel selection and transmit such indication to thehome TV set 130, and the TV 130 may switch to the channel “CBS 110”accordingly.

In a further implementation, the user's selection of channel “CBS 105”may be transmitted to a ATMOS, which may in turn automatically populatea message on social media, e.g., a Facebook status update showing theuser “is watching The Big Bang Theory on CBS 135.” In an alternativeimplementation, the user may elect to manually enter and populate thesocial media feeds via the ATMOS client component instantiated on hispersonal mobile device.

For another example, the social message, e.g., a Tweet message, maycontextually tag the text on key terms to indicate what the user iswatching, e.g., the Tweet hashtags “#The Big Bang Theory,” “#CBS,” etc.In one implementation, the hashtags may link to profile information of aTV show, including its scheduled airing time, crew information,description, and/or the like. In further implementations, the Tweethashtags may be employed for social content data taxonomy engine, asfurther illustrated in FIGS. 6B-6E.

FIG. 1B shows a block diagram illustrating an example embodiment ofaudience attendance monitoring within embodiments of the ATMOS. Withinimplementations, ATMOS may monitor the audience attendance of selectedTV program. For example, upon a user selecting a TV channel at hispersonal mobile device 115 (e.g., by tapping on the channel listing asshown at 105 in FIG. 1A), the user may or may not be present watchingthe TV program broadcasting on the channel, e.g., the user may stepaway, may engage in other activities distracting him from the TV, mayinvite one or more other audience watching the selected TV channel, etc.In such scenarios, ATMOS may constantly, periodically and/orintermittently “monitor” the audience status to obtain knowledge of theaudience viewing data.

As shown in one example in FIG. 1B, the personal mobile device 115 mayautomatically snap a photo and/or a video clip of the audience sceneincluding the audience 118 watching the TV program 110. For example, theATMOS may be configured to snap a photo or video clip periodically(e.g., every 10 minutes, etc.). The ATMOS may then packetize theobtained photo/video clip as audience atmospherics data 125 for audienceattendance analysis.

In further implementations, ATMOS may include various data into theatmospherics data packets. For example, ATMOS may “listen” and record anaudio clip of the audience scene. For another example, ATMOS may promptthe user to indicate how many audience are present to watch the on-goingTV program. For another example, ATMOS may monitor whether the user isengaging in other application activities on the personal mobile device115, e.g., email(s), Facebook, browser activities, gaming applications,etc. For another example, ATMOS may include GPS information of thepersonal mobile device 115 into the atmospherics data.

In one implementation, ATMOS may be configured to automatically createphoto/video/audio captures. In another implementation, ATMOS may promptthe user to manually enter desired information, such as how manyindividuals are present to watch the TV program, and/or request the userposition the built-in camera of the mobile device to snap photos/videoclips. In one implementation, ATMOS may provide promotion incentives forthe user to cooperate with such requests, e.g., lottery opportunities,etc.

In one implementation, ATMOS may analyze the obtained audienceatmospherics data 125. For example, as shown in FIG. 1B, ATMOS mayidentify the number of audience 138 at an associated timestamp from anaudience scene photo/video 135 via face recognition software, e.g.,Apple iPhoto face recognition, etc.

FIG. 1C shows an example of intelligent mobile questionnaire withinimplementations of the ATMOS. In one implementation, ATMOS may sendsynchronized questionnaire to a user's personal mobile device 115 basedon the media program on-air at the user's selected TV channel. Forexample, if the user's selected TV channel 130 includes an advertisement145 of Audi automobiles, ATMOS may generate a question 123 to inquirethe user's desired automobile brand. Such inquiry results may becollected by ATMOS and fed to the advertising brand merchant 150 (e.g.,Audi, etc.) to determine performance of the advertisement 145. Forexample, if the user selects “Audi” when inquired about his desiredautomobile brand after watching the Audi advertisement channel, it mayshow effectiveness of the advertisement 155 over the TV channel. Infurther implementations, ATMOS may include product placementadvertisements, and/or the like in the pop-up questionnaire 123, asfurther discussed in FIGS. 2 and 7B.

FIG. 1D shows an example of media analytics within embodiments of theATMOS. In one implementation, a ATMOS client may desire to know publicopinions of their products, e.g., brand name products, TV programs,and/or the like. For example, as shown in FIG. 1D, the producer “CBS,”e.g., merchant 150, may want to know audience's reaction about theproduction “The Big Bang Theory” 180. In one implementation, the ATMOSmay collect data from social media platforms, such as, but not limitedto Twitter, Facebook, and/or the like, user comments and activities withregard to the show “The Big Bang Theory.” For example, ATMOS may obtainTweets about users' comments on “The Big Bang Theory” 185. For anotherexample, ATMOS may obtain Facebook user comments, activities (e.g., user“like” of the “The Big Bang Theory” page, news feed, etc.) fromFacebook. Further implementations of obtaining data from social mediaplatforms are discussed in FIGS. 5A-5F.

In one implementation, ATMOS may generate a media analytics report 190based on the obtained social media user comments to reflect audiencereaction to the show “The Big Bang Theory.” For example, in oneimplementation, the report may comprise statistical data with regard toaudience age, demographics, occupation, etc. Further examples of mediaanalytics report are discussed in FIG. 8C.

FIG. 2A shows a block diagram illustrating data flows between NR-Controlserver and affiliated entities within various embodiments of the ATMOS.Within various embodiments, one or more users 233 a, ATMOS server 220,social media 250, TV network 270, ATMOS database(s) 219, merchant 280,and/or social media 250 are shown to interact via various communicationnetwork 213.

In one embodiment, the ATMOS may receive a list of real time TV program237 a. For example, in one implementation, the TV program 237 data maycomprise information such as channel information, media programinformation of each channel, program schedule information, and/or thelike. For example, the TV network 270 may provide a (Secure) HypertextTransfer Protocol (“HTTP(S)”) PUT message including the TV schedule data237 a in the form of data formatted according to the eXtensible MarkupLanguage (“XML”). Below is an example HTTP(S) PUT message including anXML-formatted TV schedule for the ATMOS server:

PUT /TVschedule.php HTTP/1.1 Host: www.TV.com Content-Type:Application/XML Content-Length: 718 <?XML version = “1.0” encoding =“UTF-8”?> <TVSchedule> <Date> 09-09-2000 </Date> <Channel1> <ChannelID>CH001 </ChannelID> <ChannelName> CBS </ChannelName> <Program1><StartTime> 9:00:00 </StartTime> <MediaID> 1234456 </MediaID> <EndTime>9:45:00 </EndTime> </Program1> <Program2> ... </Program2> ...</Channel1> <Channel2> ... </Channel2> ... </TVSchedule>

The media program may further comprise information such as media airtime, media length, advertisement tag timestamp, ad name, ad productinformation, and/or the like. The media program may further comprise asub-table comprising embedded advertisement tags (e.g., see 343 a inFIG. 3A).

In further implementations, the TV schedule 237 may comprise sub-tablesincluding information with regard to the media programs. For example, anexemplary XML-formatted TV program table takes a form similar to thefollowing:

<TVProgram> <ID> 00001 </ID> <Name> The Big Bang Theory </Name> <Genre>Comedy </Genre> <Networks> CBS </Networks> <Actors> <Actor1> Jim Parsons</Actor1> ... </Actors> <FirstRun> 08-2008 </FirstRun> <AiringTime><StartTime> 9:00:00 </StartTime> <EndTime> 9:45:00 </EndTime> <Weekday>Thursday </Weekday> <Frequency> Weekly </Frequency> ... </AiringTime><KeyWords> <KeyWord1> Geeks </Keyword1> <KeyWord2> Physicists</Keyword2> ... </KeyWords> <Characters> <Character1> Sheldon Cooper</Character1> <Character2> Penny </Character2> ... </Characters> ...</TVProgram>

In one embodiment, the user 233 a may operate a client mobile device,which may receive a list of real time TV programs 237 b. In oneembodiment, upon reviewing the received channel schedule 237 b, the usermay submit a channel selection by tapping on a selected channel (e.g.,see 105 in FIG. 1B). In one implementation, the user mobile device maybe enabled with an infrared remote control component (e.g., a plug-inaccessory 120 in FIG. 1B), and may send the channel submissionindication 235 a to a client TV 233 b (e.g., a home TV set) via aninfrared communication channel, which may result in a channel switch onTV. In another embodiment, when the user selects the channel from hismobile device (e.g., an Apple iPhone, etc.), the channel selectionmessage 235 b may be transmitted to the ATMOS server 220 in real time.

In another implementation, the ATMOS server 220 may provide the TVprogram schedule data to a TV set-top box (STB) 201, e.g., via a cablenetwork, wherein the STB may receive user TV event messages 235 a andforward such information 235 c to the ATMOS server 220. In anotherimplementation, the STB 201 may directly communicate with a ATMOSinfrared component (e.g., 120 in FIG. 1B), a table unit, and/or thelike. In one implementation, the TV event 235 a/b/c may comprise avariety of TV events, such as, but not limited to TV on/off, STB on/off,channel switch, and/or the like. In further implementations, when theuser has registered non-live media facility (e.g., DVR, DVD, TiVo, etc.)with the ATMOS, the TV event 235 a/b/c may comprise DVD player on/off,TiVo on/off, TiVo channel change, and/or the like. In oneimplementation, to operate and exchange data with the STB, thepresentation layer on the user mobile device may adopt developmenttools, such as but not limited to Android, iOS app development tools,and/or the like. In one implementation, the TV set-up top box maysimilarly employ a presentation layer development tool compatible withthat of the user mobile device, and may additionally employ HTML5 andweb 2.0 presentation layers.

In one implementation, a TV channel selection event 235 b may be loggedby the ATMOS server 220 and stored as a real time data record in a ATMOSdatabase 219. For example, in one implementation, the user device mayprovide a HTTPS POST message including the TV channel selection message235 b in the form of data formatted according to the XML. Below is anexample HTTP(S) POST message including an XML-formatted user trigger forthe ATMOS server:

PUT /CHsubmission.php HTTP/1.1 Host: 255.000.00.1 Content-Type:Application/XML Content-Length: 701 <?XML version = “1.0” encoding =“UTF-8”?> <CHSelectionEvent> <EventID> 1111 <EventID> <EventType> CHSwitch </EventType> <Time> 19:00:00 </Time> <Date> 09-09-2000 </Date><UserID> JDoe </USerID> <UserName> John Doe </UserName> <DeviceID>JD0001 </DeviceID> <IP> 255.000.00.1 </IP> <HardwareID> 00001</HardwareID> <AppID> JDDOEMOBILE 00001 </AppID> <MAC> 00:00:00:00:00:00</MAC> <Channel> <ChannelName> CBS </ChannelName> <Category> Comedy</Category> <Program> The Big Bang Theory> </Program> <Episode> 4-1</Episode> <StartTime> 19:00:00 </StartTime> <EndTime> 19:29:45</EndTime> <Duration> 29′45″ </Duration> </Channel> ...</CHSelectionEvent>

In further implementation, the user's mobile device may send eventmessages. Such event messages may include channel selection message 235b, user checkin/checkout action (e.g., user signing in/out to a ATMOSmobile/web-based client portal, etc.), and/or the like. In oneimplementation, the events may be sent to the ATMOS server 220 via HttpsPost web based API, which may comprise a type identifier and a set ofparameters of the event data, e.g., channel selection, user response,etc. In one implementation, ATMOS server 220 may save such event data atCSV format. For example, the following Tables I and II provide anexemplary data structure of an event message:

TABLE 1 Watching Event Data Structure User Action Event Type ParametersATMOS App Started 1 EventID, UserID Pulse Event (keep alive 2 EventID,userID, zipcode, ping, sent every 10 min) SourceID, time-zone, isSocial,tmsID, timestamp Change change 3 Same as Type 2 Check in 10 Same as Type2 Check out 11 Same as Type 2

TABLE 2 Event Data Parameters Parameter Type Details EventID INT Uniqueidentifier that is generated for each event that is added to the DBUserID String Unique identifier for a ATMOS user. The client app maysend the device UDID. Zipcode INT Zipcode as entered by the user at theclient app SourceID INT An ID in a media service program that identifiesa channel Time-zone INT The offset from GMT tmsID String Uniqueidentifier of a program (an episode or a program with a singleoccurrence). Using the tms data, these can be mapped also to theshow/series isSocial INT A boolean that is used to filter social eventdata from being shared with friends (e.g., see 275a)

In one embodiment, upon submitting a channel selection, the user 233 amay populate social media feeds of his viewing status 275 a to thesocial media network 250, wherein the user's friends may view hisstatus, knowing what the user is watching, electing to “like,”“dislike,” and/or comment on his status, and/or the like. For example,in one implementation, a ATMOS client app may comprise a social feature(e.g., see 715 in FIG. 7A) so that a user may input a social mediamessage (e.g., 715 b/c in FIG. 7A). In another implementation, the ATMOSmay automatically generate a social message based on a pre-populatedmessage format (e.g., a Tweet format with hashtags, etc.) send a messageindicating the user's viewing status 275 b to the social media 250,which may automatically populate a social media status update. In suchcases, the social media platform 250 may request user authorization forATMOS server 220 to access, e.g., download social data or send socialdata to social media, etc. The ATMOS may obtain a user token forauthorization to access the user's social media profile, content, and/orthe like. The user authorization may be further discussed in FIGS.5A-5C.

For example, in one implementation, the ATMOS may provide a HTTPS POSTmessage including a social message 275 b in the form of data formattedaccording to the XML. Below is an example HTTP(S) POST message includingan XML-formatted user viewing status 275 a/b for the ATMOS server:

PUT /UserSocial.php HTTP/1.1 Host: www.ATMOS.com Destination:www.Facebook.com Content-Type: Application/XML Content-Length: 701 <?XMLversion = “1.0” encoding = “UTF-8”?> <SocialMessage> <MsgID>Facebook1111 <MsgID> <Time> 19:00:00 </Time> <Date> 09-09-2000 </Date><UserID> JDoe </USerID> <UserName> John Doe </UserName> <UserTokenID>12324 </UserTokenID> <AppID> MR0001 </AppID> <Action> <ActionType>Status Update </ActionType> <Content> ″is watching The Big Bang Theoryon CBS″ </Content> <Rating> N/A </Rating> ... </Action> <Tags> <Tag1>Big Bang Theory </Tag1> <Tag2> CBS </Tag2> ... </Tags> ...</SocialMessage>

In the above example, the ATMOS server 220 may automatically populate asocial message to the user's Facebook page, showing a status update“John Doe is watching The Big Bang Theory on CBS.”

In a further implementation, the user 233 a may receive friends'recommendations 277 of TV programs from the social media 250 (e.g., see705 in FIG. 7A). For example, the user may view a scroll-down list offriends' recommendations from a user interface via the user's mobiledevice.

In further embodiments, ATMOS may load data 278 from the social mediaplatform 250, e.g., user profile information, user comments/activitydata related to an advertisement/a TV program, and/or the like. Furtherimplementations and example data structures of the social media data 278are discussed in FIG. 5F.

FIG. 2B shows a block diagram illustrating data flows between ATMOSentities within alternative embodiments of the ATMOS. Withinimplementations, user mobile device 233 a may perform “ambientlistening” on the user. For example, when a user has submitted a channelselection, it is not guaranteed whether the user is indeed watching theTV program selected, e.g., the user may walk away, engaging in otheractivities such as playing video games, chatting with others, etc. Assuch, the user's mobile device 233 a may “listen-in” the user'sbehavior, e.g., by capturing live video of the surroundings of the user,by recording audio clips, by capturing a picture, and/or the like, tocapture information as to whether and/or what the user is watching theTV. In one implementation, such “listen-in” data (e.g., video clips,audio clips, pictures, device application status, GPS coordinates, etc.)may be aggregated and packetized as atmospherics data 239 andtransmitted to the ATMOS server 220, which may analyze such atmosphericsdata to determine whether the user is watching, and integrate theatmospherics analytics in an audience statistics report 245. In oneimplementation, the ATMOS may provide the audience statistics report 245to the TV network 270 for TV program audience feedbacks.

For another example, in one implementation, the user device 233 a maygenerate an atmospherics data package as a HTTPS POST message in theform of data formatted according to the XML. Below is an example HTTP(S)POST message including an XML-formatted atmospherics data 239 to provideto the ATMOS server 220:

PUT /Listen-in.php HTTP/1.1 Host: 255.000.00.1 Content-Type:Application/XML Content-Length: 701 <?XML version = “1.0” encoding =“UTF-8”?> <Atmospherics> <AtmosID> AT0003 </AtmosID> <AtmosType>Listen-in </AtmosType> ... <Time> 19:00:00 </Time> <Date> 09-09-2000</Date> <UserID> JDoe </USerID> <UserName> John Doe </UserName><DeviceID> JD0001 </DeviceID> <IP> 255.000.00.1 </IP> <HardwareID> 00001</HardwareID> <AppID> JDDOEMOBILE 00001 </AppID> <MAC> 00:00:00:00:00:00</MAC> <Data1> <Type> audio </Type> <FileFormat> MP3 </FileFormat><FileName> MyRecord </FileName> <Length> 10s </Length> <Size> 567 KB</Size> ... </Data1> <Data2> <Type> Image </Type> <Format> JPEG</Format> <FileName> MyPic </FileName> <Exif> <Source> iPhone </Source><Timestamp> 19:00:00 <Timestamp> <size> 1024 X 768 </size> <focus>600/100 </focus> ... </Exif> ... <Data2> <Data3> <Type> GPS </Type><Content> 45 Garden Street </GPS> ... </Data3> <Data4> <Type> Device App</Type> <Timestamp> 19:00:00 </Timestamp> <App1> <Name> Angry Bird</Name> <Type> Gaming </Type> <Status> Active </Status> ... </App1></Data4> ... </Atmospherics>

The user device may perform “ambient listening” and generateatmospherics data package constantly, intermittently, and/orperiodically (e.g., every hour, etc.) to “listen-in” user's watchingstatus, e.g., whether the user is paying attention to the selected TVprogram. In the above example, the generated atmospherics data packagemay comprise a variety of data segments, such as a “MyRecord.mp3” audioclip which may indicate whether the played audio matches a selectedchannel program, whether the user is chatting with friends (e.g., see298 at FIG. 2D); a “MyPic.JPEG” photo taken by an iPhone camera, whichmay indicate whether the user is present with the TV set, and/or one ormore individuals are present (e.g., see 297 at FIG. 2D); GPS informationof the user mobile device which may indicate whether the user is presentat his residential address where the home TV is located (e.g., see 299 cat FIG. 2D); user mobile device application status information includingan actively running gaming application (e.g., “Angry Bird”), which mayindicate the user is playing the video game instead of paying attentionto the TV program (e.g., see 299 a at FIG. 2D). In one implementation,the atmospherics data 239 a and/or analytics may be incorporated into adata record 290 a and stored in the database 219.

In another implementation, the user mobile device may send a deviceapplication event 241 indicating user device application status to theATMOS server 220. For example, the device application event 241 maycomprise an actively engaged application information on the device,e.g., application ID, application name, application category, push emailheart beat pulse, etc, which may suggest audience activities whilewatching TV, e.g., video gaming, texting, calling, checking email,browsing, playing music, editing photos, and/or the like. For example,in one implementation, the user device 233 a may generate a mobileapplication event as a HTTPS POST message in the form of data formattedaccording to the XML. Below is an example HTTP(S) POST message includingan XML-formatted device application status 241 to provide to the ATMOSserver 220:

PUT /MobileApp.php HTTP/1.1 Host: 255.000.00.1 Content-Type:Application/XML Content-Length: 701 <?XML version = “1.0” encoding =“UTF-8”?> <MobileApp> <Time> 19:00:56 </Time> <Date> 09-09-2000 </Date><UserID> JDoe </USerID> <UserName> John Doe </UserName> <DeviceID>JD0001 </DeviceID> <IP> 255.000.00.1 </IP> <HardwareID> 00001</HardwareID> <AppID> JDDOEMOBILE 00001 </AppID> <MAC> 00:00:00:00:00:00</MAC> <EventType> gaming </EventType> <App1> <Name> Angry Bird </Name><Type> Gaming </Type> <Status> Active </Status> <StartTime> 19:00:45</StartTime> <EndTime> 19:00:56 </EndTime> <Duration> o′11″ </Duration>... </App1>

In one implementation, the device application status 241 may comprise alist of application names that have been open and active for a minimumamount of time (e.g., 10 seconds, etc.). In another implementation, thedevice application status 241 may be periodically harvested to the ATMOSserver.

FIG. 2C provides a logic diagram illustrating user-server interactionswithin embodiments of the ATMOS. In one embodiment, a user mayinstantiate a ATMOS application component on a mobile device 250. Theuser may also plug a ATMOS accessory (e.g., 120 in FIG. 1) into hismobile device to facilitate communication between the mobile device anda home TV set.

In one implementation, the user may submit a session request 253 to theATMOS server 220 via the mobile application, e.g., as shown in Table 1,the session request 253 may comprise a user ID and an event/session IDto indicate the ATMOS application has stared. Upon receiving a userrequest, the ATMOS server 220 may determine whether the user hasregistered with ATMOS 255. For example, in one implementation, the usermay be a new user to the ATMOS application service, who may justdownload and install the mobile application but has not registered. Ifthe user is not registered 258, the user may be requested to submitregistration information, such as user name, phone number, emailaddress, residential address, and/or the like for registration 260. TheATMOS may also obtain a physical address, a hardware ID of the usermobile device, etc., for registration purposes.

In one implementation, upon registration, the ATMOS server 220 mayobtain and store a user application ID, and/or a session ID 265 to startthe ATMOS service session. In one implementation, the ATMOS server 220may obtain a real-time TV program listing 267, e.g., from a TV network,a TV broadcasting service, etc. In one implementation, the ATMOS server220 may obtain the TV schedule updates on a periodical basis (e.g.,daily, etc.), and store the TV schedule information at a TV scheduledatabase. In one implementation, the user 233 a may receive a list of TVprograms 270 via the ATMOS application, e.g., see 710 in FIG. 7A.

In one implementation, the user mobile device may send a user eventmessage 280 to the ATMOS server 220, and the ATMOS server may monitoruser event messages 276 from the user device. As discussed in FIGS.2A-2B and will be further discussed in FIGS. 3A and 5A, the receiveduser event at 276 may comprise a variety of events/messages, such as,but not limited to user TV event (e.g., 235 b in FIG. 2A), user deviceapplication event (e.g., 241 in FIG. 2B), atmospherics data (e.g., 239in FIG. 2B), social content (e.g., 525 a/b in FIG. 5A), response toads/surveys (e.g., 339 in FIG. 3A), and/or the like. In oneimplementation, upon receiving the message, the ATMOS may determine atype of the message 288. In one implementation, the user event messagemay comprise user channel submission event 289 b, check-in/out messages,and/or the like, e.g., see 235 a in FIG. 2A. For another example, theuser event message may comprise atmospherics data 289 a (as furtherillustrated in FIG. 2E), ad/survey response 289 c (as furtherillustrated in FIGS. 3B-3C), social data 289 d (as further illustratedin FIG. 5B), non-live TV consumption message 289 e (as illustrated inFIG. 2G. For example, in one implementation, the ATMOS may determine themessage types 289 a-e based on the data structure, e.g., a field valueof a field “message type” as shown in Table 2, message type code in theheader information of a received data packet, and/or the like.

FIG. 2D provides a logic flow diagram illustrating TV channel submission(e.g., 289 b/e) message (for live TV, and/or non-live TV program such asDVD playing, TiVo, media on demand, and/or the like) processing withinimplementations of the ATMOS. In one implementation, upon receiving achannel TV event message at 289 b/e, the ATMOS server may parse thereceived data message 2001 and save it in a raw data message database,e.g., as XML-formatted records in 219 a. For example, a variety ofexemplary XML-formatted TV event message data structures (e.g., TVon/off, STB on/off, DVD on/off, channel change, etc.) are discussed at235 a in FIG. 2A.

Within implementations, the ATMOS may determine whether each received TVevent message indicates a stable TV channel program selection, or achannel surfing. In one implementation, the ATMOS retrieve a list ofunprocessed TV message records (e.g., grouped per user profile) 2002from the raw data store 219 a. For every two consecutively receivedmessages 2005, the ATMOS may calculate the elapsed time in-between 2008,and determine whether the elapse time is greater than a pre-determinedsurfing threshold (e.g., 5 seconds, 10 seconds, etc.). For example, whenthe elapsed time is shorter than the surfing threshold, indicating theuser may be frequently switching channels to browse the program, theATMOS may not consider the channel selection message as effective TVviewing, and may filter such message records from TV viewing analysis2013.

In another implementation, when the elapsed time is greater than thesurfing threshold 2009, suggesting the user may at least spend an amountof time staying on the selected channel, the ATMOS may further determinewhether the elapsed time is greater than a capping threshold 2015. Forexample, when the elapsed time is too long, greater than the cappingthreshold (e.g., 2 hours, 3 hours, etc.), it may suggest a user may justlet the TV on without watching. In such cases, ATMOS may apply cappingedit rules to compute a “real” watching time 2025. For example, if theelapsed time between a first TV channel switch and a second channelswitch is 5 hours, the ATMOS may not log 5 hours as the watching timefor the first selected channel, as the capping threshold is 2 hours. TheATMOS may in turn determine the watching time of the first selectedchannel as capped by a STB/TV off event (e.g., when a STB/TV off eventis received during the elapsed time, the watching time may not exceedthe timestamp of the STB/TV off event), TV program end time (e.g., whenthe playing TV program on the first selected channel ends during theelapsed time, the watching time is calculated as the time elapse betweenthe first TV channel event and the TV program end time), and/or thelike, e.g., at 2023. In further implementations, the ATMOS may applycutoff thresholds based on historical heuristics via statisticalanalysis 2023. For example, the ATMOS may determine the watching timebased on individual habits, e.g., a user has been observed to stay onthe same channel for at most 1 hour, etc. For another example, thecut-off threshold may be analyzed by channel, e.g., 1 hour on CBS, 2hours on ABC family, and/or the like.

In one implementation, when the elapsed time does not exceed a cappingthreshold at 2015, and/or when the watching time has been re-calculatedbased on capping rules at 2025, the ATMOS may retrieve TV programinformation on the user selected channel 2018, and generate a userchannel selection log file 2020. For example, the log file may comprisefields such as user ID, channel ID, channel selection time, userwatching time, channel program name, channel program ID, and/or thelike. Such generated log files may be fed to a user TV viewing dataengine 2030 for audience analytics 219 b. For example, the audienceanalytics database 219 b may be utilized to analyze TV viewing rates ofa TV program, product/brand impression of advertised products during theuser watching time, and/or the like. Exemplary audience analyticsreports are discussed at FIG. 10A-10H.

the ATMOS server 220 may retrieve TV program information to determinethe TV program played on the user selected channel 292. For example, theATMOS may query on a TV program table (e.g., obtained at 267 at FIG. 2C)based on an instant timestamp and the user selected channel. The ATMOSmay log the user channel selection with the timestamp 292 in a database,e.g., at 290 a in FIG. 2A.

In one implementation, ATMOS may generate TV viewing data for theretrieve TV program 293. In one implementation, ATMOS may associate theuser selection to the TV viewing rate of the retrieved TV program, andmay refine the TV viewing rate with atmospherics analytics at 306. Forexample, in one implementation, the ATMOS may monitor groups ofaudience's channel selection, wherein the audience groups may be definedbased on age, geography, and/or the like. In one implementation, theATMOS may generate an audience summary via a dashboard, e.g., see FIG.8H.

FIG. 2E provides a logic flow diagram illustrating atmospherics data(e.g., 289 a) analytics within implementations of the ATMOS. In oneimplementation, upon receiving an atmospherics data message 289 a, theATMOS server may decode the atmospherics data and obtain atmosphericsartifacts 296 a, e.g., a photo, an audio clip, and/or the like. In oneimplementation, for each decoded artifact, the ATMOS may determine theartifact type 296 b, e.g., based on the file name, file extension, etc.

In another implementation, the ATMOS may incorporate the received deviceapplication status (e.g., 289 f) for user activity analytics. Forexample, the ATMOS may capture active application s running on thedevice 299 a from the received device application data, and determineuser activities when viewing 299 b, e.g., emailing, browsing Internetcontent, texting, video gaming, and/or the like. Such indicated useractivity data may be incorporated into audience attendance estimation2100.

In one implementation, the artifact may comprise a visual data file 297,such as a video file (e.g., “wmv,” “mp4,” “avi,” “rm,” etc.), an imagefile (e.g., “JPEG,” “bmp,” “tiff,” “gif,” etc.), and/or the like. In oneimplementation, the ATMOS may determine graphical content 297 of thevisual file. For example, the ATMOS may perform image analysis todetermine whether the photo image, and/or video frames comprise a sceneof audience, and/or a TV screen. In one implementation, a mobileapplication at the user mobile device, e.g., iPhone, etc., may performface recognition at a photo taken at an iPhone, and integrate such datain the atmospherics data package, e.g., an iPhoto including two faces,etc. In another implementation, the ATMOS server may perform facialrecognition to determine audience presence 297 a. In otherimplementations, the ATMOS server may perform image analysis todetermine user activities in the photo image, e.g., reading a book,doing housework, and/or the like. In further implementations, a ATMOSpanelist may review the photo image and determine audience status.

In another implementation, the ATMOS may determine whether a TV screenimage matches the TV program associated with the user channel selection297 b, e.g., the user may switch to watch recorded program (e.g., TiVo,DVD player, etc.) instead of live TV program on the channel, and in suchcases, the ATMOS may not receive an indication of such change. Forexample, in one implementation, ATMOS may perform image analysis todetermine whether the received image photo (and/or a video frame grab)contains a TV screen shot 297 b, e.g., by detecting edges of arectangular shaped object on the image, etc. For another example, theATMOS may store a plurality of sample screen shots from the TV programplayed at the user submitted channel, and may compare the received imagephoto with each of the stored sample screen shots. In furtherimplementations, a ATMOS panelist may assist in reviewing anddetermining whether the user TV screen matches the played TV program. Inone implementation, if the ATMOS determines the user is absent from a TVset, or engaging in other activities from the graphic analysis, ATMOSmay generate negative heuristics with regard to TV viewing data of theTV program on air.

In another implementation, if the received atmospherics data comprisesan audio artifact (e.g., with a file extension of “way,” “mp3,” “ape,”“m4a,” etc.), the ATMOS may perform audio analysis to determine acontent of the audio 298. For example, the ATMOS may analyze thefrequency range of the audio content to determine the sound source,e.g., whether it is human voice, ambient noise, media broadcasting,and/or the like.

In one implementation, if the audio content comprises human voice (e.g.,within the frequency range 60˜7000 Hz), the ATMOS may determine whetherthe human voice is from the audience or broadcasting media. For example,the ATMOS may perform voice recognition to determine whether the humanvoice matches with any of the characters in the TV program on air on theuser submitted channel, e.g., at 298 a.1. If not, the ATMOS maydetermine such human voices may indicate audience presence 298 a, e.g.,whether more than one user is present with the TV set.

In further implementations, if the audio file comprises human voice, theATMOS may extract verbal content from the audio file to determinewhether an audience conversation, or a human conversation from mediaplaying, is related to the TV program on air on the user submittedchannel 298 a.2. For example, the ATMOS may adopt speech recognitionsoftware (e.g., Nuance, IBM WebSphere Voice, etc.) to extract key termsfrom the conversation, and compare whether the key terms are related tokey words stored with the TV program in the database. For example, ifATMOS extracts key terms “quantum mechanics,” “physics,” “big bang,”etc., from the human conversation in the received atmospherics audioartifact, and the user submitted channel CBS is playing “The Big BangTheory,” ATMOS may determine the audience is watching the show on air.In such cases, the ATMOS may not need to distinguish whether the humanconversation in the audio file is from the audience or TV, but focus onmining the content of the conversation.

In another implementation, if the ATMOS determines the audio artifactcomprises ambient noise, ATMOS may determine an environment of theaudience 298 b. For example, if the background is overly noisy, the usermay not be watching the TV program.

In another implementation, if the ATMOS determines the audio artifactcomprises media sound (e.g., music, etc.), the ATMOS may determinewhether the audio media content matches the TV program on air on theuser submitted channel 298 c via a media recognition software (e.g., aShazam alike music recognition kit, etc.). For example, if the userselected channel CBS has “The Big Bang Theory” scheduled at the moment,but the ATMOS determines a Lady Gaga song in the received audioatmospherics, this may indicate the user is not watching the TV program.In one implementation, if the ATMOS determines the user is distractedfrom the TV program based on the audio content, ATMOS may generatenegative heuristics with regard to TV viewing data of the TV program onair.

In an alternative implementation, the ATMOS client component, which maybe instantiated on a user mobile device (e.g., a downloadableapplication such as, but not limited to an iPhone application, anAndroid application, etc.) and/or a table top standalone unit, mayobtain atmospherics data to determine the program the user is watchingwithout user indication of the channel. For example, the ATMOS componentmay obtain audio recording, video recording, signatures image captures,and/or the like of the audience watching environment, and submit theobtained data to ATMOS server. In one implementation, the ATMOS servermay analyze the obtained data to determine what the audience is watchingin a similar manner as illustrated at 297, 298 in FIG. 2E, but withoutthe user's channel selection indication.

In one implementation, the ATMOS may perform an audio/video recognitionprocedure to identify a TV program, e.g., via digital signaturesembedded in the program, and/or the like. In another implementation, theATMOS may extract key terms from the audio/video captures, and form aquery on a database of TV programs to find a match. For example, in oneimplementation, the ATMOS may extract textual terms from the obtainedaudio media program excerpts, such as “big bang,” “quantum physics,”“Sheldon,” etc., and may form a query in a database of TV programs whichmay return a result indicating the obtained audio media program excerptmay be related to the TV show “Big Bang Theory.”

In an alternative implementation, rather than uploading sampling and/orthe entirety of the captured audio/video median content, the ATMOS mayanalyze the recorded audio/video content to generate a unique signatureand/or a unique hash, which may be used for further matching. Forexample, the unique signature/hash may take a form similar to a sequenceof 0-1 representation of a sampling of the recorded media content. Inone implementation, the signature/hash generation may be performed at auser device (e.g., the user's mobile phone, the table top unit, etc.),which may upload the generated signature/hash sequence to the ATMOSserver. In another implementation, the user device may upload mediacontent sampling, clips or the captured entirety to the ATMOS server,which may then perform the signature generation. Within implementations,software tools/components such as, but not limited to i-brainz, and/orthe like may be adopted to generate audio signature/hash.

In further implementations, such “listen-in” activities may be performedon a standalone table unit, which may communicate with a user computervia a wireless network, and/or transmit the “listen-in” results to theATMOS server.

In further implementations, lighting sensor data 299 c may indicate thelighting condition of the user environment to determine the viewingstatus. GPS information contained in the atmospherics data may indicatewhether the user is located with the home TV 299 d, e.g., by comparingthe instant GPS location with a registered user residential address,etc.

In one implementation, ATMOS may analyze the variety of atmosphericsdata to determine whether the user should be accorded as a “viewer” ofthe real-time TV program on air. In one implementation, as shown in FIG.2H, the ATMOS server may adopt a procedure to generate a progressiveweighted sum of atmospherics scores to determine whether the user is“watching” or “not watching.” When the weighted sum of differentatmospherics scores exceeds a predetermined threshold, the user may beconsidered “not watching,” and the ATMOS may not need to proceed withfurther atmospherics analysis to improve efficiency of the atmosphericsanalytics.

For example, at 299 d in FIG. 2H (e.g., comparing received user GPSinformation with the user's registered residential address in FIG. 2D),the ATMOS may determine an address type 2163, and may assign a weight tothe GPS atmospherics factor based on the address type from anatmospherics GPS weight table 2164. For example, the atmospherics GPSweight table may assign 0.1 to a residential address, 0.2 to a Starbucksstore, 1.0 to an outdoor address (e.g., a national park, etc.), and/orthe like. In one example, the ATMOS may assign a GPS factor weighingscore similar to the following table:

TABLE 3 GPS Information Weighing Scores GPS Address Type Weighing ScoreResidential address 0.1 Commercial Coffee Shop, 0.5 Address RestaurantsHotel 0.1 . . . . . . Shopping Site 0.5 Outdoor Highway 1   AddressNational 0.8 Parks . . . . . .

In one implementation, the ATMOS may calculate the atmospherics score2200, which may be the assigned GPS weight at this stage, and determinewhether the score is greater than a predetermined threshold (e.g., 1,etc.) 2205. If so, the ATMOS may conclude the user is not watching theTV and quit scoring 2300. Otherwise, the ATMOS may proceed withanalyzing device app analytics data at 299 a, and repeat the process ofupdating atmospherics score to determine whether a threshold has beenmet to suggest the user is not watching.

At 2165, the ATMOS may determine an active application type 2165, andassign a weight based on the application type 2168. For example, anactive gaming application may be accorded 0.7; an active emailapplication may be accorded 0.5; an active internet browser may beaccorded 0.5, and/or the like:

TABLE 4 Device Application Activity Weighing Scores Active ApplicationType Weighing Score Gaming Application 0.7 Email New Email 0.4Application Window Inbox 0.1 . . . . . . Internet Opening New 0.4Browser Link Scrolling 0.4 Down . . . . . . P2P Chatting 0.4 MessengerDialing 0.5 Software . . . . . . Office Text Editor 0.2 Application . .. . . . . . .

In one implementation, the ATMOS may calculate an updated atmosphericsscore 2170 and determine whether it exceeds the threshold 2175. If ithas not exceeded the threshold, the ATMOS may proceed with audioanalytics results from 298, provided such audio analytics is availablefrom the atmospherics data. In one implementation, the ATMOS mayretrieve and/or determine audio analytics indications 2178, e.g.,ambient noise level, media music which does not match the TV program onair, human voice chatting on irrelevant topics, etc. In one example, theATMOS may assign a weight based on audio analytics indications 2180similar to the following table:

TABLE 5 Audio Heuristics Weighing Scores Audio Heuristics Type WeighingScore Ambient Noise Loud 0.5 Level Medium 0.2 Minor 0.02 . . . . . .Human Voice Key Terms 0 Matches TV program Key Term not 0.3 Match . . .. . . Media Sound Matches TV 0 program Not match 0.3 . . . . . .

In one implementation, the ATMOS may update the atmospherics score 2185by adding the audio scores to determine whether it exceeds the threshold2190. Otherwise, ATMOS may proceed to perform graphical analytics at 297a/b given such visual data is available. In one implementation, theATMOS may retrieve and/or determine visual analytics indications 2192,e.g., user activity, user presence, number of individuals, TVscreenshot, etc. In one example, the ATMOS may assign a weight based onvisual analytics indications 2195 similar to the following table:

TABLE 6 Visual Heuristics Weighing Scores Visual Heuristics TypeWeighing Score Audience None 0.5 Presence More than 1 0 (count 2viewers) . . . . . . Audience Reading etc. 0.4 Activity Housework 0.3Working 0.5 TV Screen Matches the 0 Shot TV program Does not 0.3 Match .. . . . .

In one implementation, if the updated score 2193 does not exceed thethreshold, the ATMOS may conclude the user is watching the TV program2305, and feed such indication to 2100 for studying TV viewing rates.Otherwise, the ATMOS may conclude the user is not watching 2300, and maynot count the user as a “viewer” of the TV program.

In one implementation, the ATMOS may process the decoded atmosphericsdata based on a progressive mechanism, to reduce processing complexity.For example, as shown in FIG. 2I, the ATMOS may start with a lesscomplicated analysis of GPS information, device application status, andprogressively proceed with visual data processing with a highercomplexity.

FIG. 2F shows a logic diagram illustrating user-server interactions of“listen-in” within alternative embodiments of the ATMOS. In oneembodiment, the ATMOS may instantiate “listen-in” when a user hassubmitted a channel selection to determine whether the user is watchingthe selected channel. In another implementation, the ATMOS mayperiodically check the listen-in data (e.g., every 30 minutes, etc.).

Within embodiments, the user's mobile device may capture image, audiodata, video data, GPS coordinates and/or the like 2105. In oneimplementation, the ATMOS client component may automatically configurethe user device to capture atmospherics data, e.g., obtaining GPScoordinates, capturing audio data, capturing device application statusdata, etc. In another implementation, the ATMOS client component mayprompt a request for the user to hold up the mobile device to positionthe camera for image/video capturing of the TV screen, the audiencescene, and/or the like.

In one implementation, the ATMOS may extract identifying informationfrom the captured monitoring data 2106, such as a hardware ID, MACaddress, and/or the like. The ATMOS may determine whether there is anyexternal event 2108, e.g., the user is sending a response to surveyquestion, submitting a channel selection, etc. If there is such externalevent 2108, the ATMOS may launch the event and embed the atmosphericsinformation into the user responses 2110 for transmission. For example,in one implementation, the embedded atmospherics data may have the sametimestamp as the original user response data payload.

In another implementation, the ATMOS may aggregate, and packetizedifferent atmospherics data 2113 in a compliant data format fortransmission to the ATMOS server (e.g., see 239 in FIG. 2B).

In one implementation, upon receiving a message from the user device2115, the ATMOS server may determine the message type 2118. In oneimplementation, if the message is an atmospherics data packet 2120, theATMOS server may decode and analyze the atmospherics data packet 2123 toextract information as to the user's viewing status. For example, theATMOS server may perform an optical character recognition (OCR)procedure on a photographic frame extracted from the receivedatmospherics data to determine whether the TV program played on TVmatches the program schedule associated with the user's selectedchannel., e.g., whether the program is “correct” 2125. For example, ifthe user has submitted a selection of channel “CBS,” the ATMOS may queryon a program table to determine that “The Big Bang Theory” shall be onair at the timestamp when the atmospherics data is received. The ATMOSmay then ascertain whether the received photo of the user's TV setindicates the show on TV is “The Big Bang Theory.”

In a further implementation, the ATMOS may determine whether the user iswatching 2128. For example, the ATMOS may perform OCR on the receivedgraphic data (e.g., photos, video clips, etc.) to determine whether theuser is present in front of the TV. For another example, the ATMOS maydetermine how many users are watching the TV program by being present.For another example, the ATMOS may determine whether the user is presentin front of his home TV by analyzing the received GPS coordinates, e.g.,when the user's GPS coordinates reflects he has migrated from his homeaddress to a second address, it may indicate the user is no longerwatching the TV program after submitting channel selection.

In a further implementation, the ATMOS may generate viewing data 2130 todetermine audience rating of a TV program, wherein analysis of theatmospherics data may contribute to the viewing statistics.

FIG. 2G provides a logic flow diagram illustrating non-live mediaconsumption message (e.g., 289 e) analytics within implementations ofthe ATMOS. In one implementation, a user may submit a non-live mediaregistration request 2150 to the ATMOS, e.g., the user may registerTiVo, etc. so that ATMOS may fold the viewing data of non-live mediainto TV viewing data analytics. In another implementation, the user mayselect a channel that comprises on-demand video service. In oneimplementation, upon sending a request, the user device may submit theuser external IP (e.g., the IP address of the user mobile device, etc.),internal IP address (e.g., the IP address of the TiVo facility, aset-top box, etc.) to the ATMOS for registration 2155. The user devicemay further provide a media access key to ATMOS as authorizationcredentials for remote monitoring of the user's non-live media facility.In further implementations, the user may configure parameters of thefacility (e.g., a set-top box, etc) as shown in FIGS. 7I-J.

In one implementation, ATMOS may register the user's non-live mediafacility 2156, and may establish a secure communication channel with thenon-live media facility. In one implementation, the ATMOS may receive anon-live media schedule 2158, which may be automatically downloaded fromthe user's non-live media (e.g., media on-demand, Internet TV streamingservice such as ABC episodes, Hulu.com, etc.), and/or provided by theuser (e.g., a list of recorded programs for replay).

In one implementation, upon receiving an indication of non-live mediaselection 289 e, the ATMOS platform may check the program table todetermine the TV program on the non-live media 2160, and log the userchannel selection of an associated TV program with a timestamp 2125. TheATMOS may also obtain and analyze atmospherics data 2123, e.g., in asimilar manner as discussed in FIG. 2D, to generate TV viewing data forthe selected TV program 2127.

FIG. 2I provides a logic flow diagram illustrating user mobile device asa TV remote (e.g., 235 a in FIG. 2A) within implementations of theATMOS. In one implementation, a user may plug a ATMOS infrared accessory(e.g., 120 in FIG. 1B) into a user mobile device 2500. The user mobiledevice may be a general purpose personal device (e.g., as opposed to adedicated TV remote facility, etc.), such as, but not limited to anApple iPhone, iTouch, iPod, iPad, BlackBerry, Palm, HTC Evo, GoogleAndroid, Samsung Galaxy, and/or the like. The user may instantiate aATMOS client component on the user device 2505 (e.g., see FIGS. 7A-7L,etc.), wherein the ATMOS client component may determine whether aninfrared plug-in is available 2510. If so, the user device may initiatean automatic scan on its communication stack for TVs/DVDs 2515. If thequery returns a TV/DVD is available 2520, the user device may obtain aphysical address of the scanned facility, and determine a type of thefacility 2523, wherein such information may be logged into acommunication stack 2528. In another implementation, if no results comeout of the automatic scan at 2520, the user device may obtain usersubmitted TV/DVD parameters 2525 (e.g., TV/DVD brand, type, etc.), andadjust the scanning mode based on the user submitted parameters 2526.

Upon establishing communication with a TV/DVD set, the user device maymonitor on user's channel submission 2530. When a channel selection isobtained, the user device may transmit a channel selection indication tothe logged TV/DVD address via the infrared plug-in accessory 2535. Inthis manner, the user may operate a general purpose mobile device as aTV/DVD remote.

FIG. 3A shows a block diagram illustrating data flows between ATMOSentities within alternative embodiments of the ATMOS. Continuing on fromFIGS. 2A and 2B, in one embodiment, the ATMOS may receive a list of realtime TV program 237 a from the TV network 270, and a list ofadvertisement tags 243 a associated with the TV programs 237 a. Forexample, for each TV media program, which may comprise both segments ofTV programs (e.g., a TV play) and a plurality of advertisements (e.g.,interleaved during the TV play broadcasting). For another example, theadvertisement tags 243 a may label embedded advertisement in a scene ofthe TV play, e.g., a pair of sunglasses carried by a character in the TVshow “The Big Bang Theory” may comprise a product placementadvertisement tag. In a further implementation, for product placements,the ad tags 343 a may comprise video frame grabs with embedded graphicindications of the placed products in the scene, e.g., see FIG. 7G.

For example, in one implementation, the TV media program table 237 a(e.g., see also 237 a in FIGS. 2A-2B) may comprise a sub-tablecomprising embedded advertisement tags. For example, in oneimplementation, an exemplary XML record of a media program datastructure with ad tags 343 a may take a form similar to the following:

PUT /AdTag.php HTTP/1.1 Host: www.TV.com Content-Type: Application/XMLContent-Length: 718 <?XML version = “1.0” encoding = “UTF-8”?> <Media><MediaID> 123456789 </MediaID> <MediaName> The Big Bang Theory</MediaName> <Content> <Season> 3 </Season> <Episode> 2 </Season><MediaLength> 68′34″ </MediaLength> <KeyWords> Vampire, Blood, South,</Keywords> ... </Content> <MediaChannel> CBS </MediaChannel><MediaAirTime> 9 pm 09/09/2000 </MediaAirTime> <MediaSource> XXXProduction </MediaSource> <MediaGenre> Comedy </MediaGenre> ... <AdTag1><AdID> M0008 </AdID> <AdType> Regular </AdType> <AdStartTimeStamp>20′34″ </AdStartTimeStamp> <AdEndTime> 22′45″ </AdEndTime> <AdSponsor>Audi </AdSponsor> <AdProductInfo> <ProductName> Audi R8 </ProductName>... </AdProductInfo> ... </AdTag1> <AdTag2> <AdID> M0009 </AdID><AdType> Product Placement </AdType> <AdStartTimeStamp> 25′54″</AdStartTimeStamp> <AdEndTime> 28′45″ </AdEndTime> <AdSponsor>XYZ-Designer </AdSponsor> <AdProductInfo> <ProductName> French StyleSunglasses </ProductName> ... </AdProductInfo> <AdPrompt> ″Do you wantto learn more about the red bag in the scene?″ </AdPrompt> <AdRedirect>www.buythings.com/XYZ/spring2000 </AdRedirect> <AdInteractive>ScreenShot.gif </AdInteractive> ... </AdTag2> ... </Media>

The above XML example shows a media program “The Big Bang Theory season3, episode 2” which is scheduled to be on air on CBS at 9 pm on Sep. 9,2000. The example media program comprise an ad tag which may be aregular advertisement (e.g., non-product placement or embedded in thescene) of Audi automobile, and another ad tag which may be an embeddedproduct placement, e.g., a “pair of XYZ-designer sunglasses” as shown ina scene during the TV program (e.g., see FIG. 7G). In a furtherimplementation, the ATMOS may redirect the user to a URL“www.buybags.com/XYZ/spring2000” if the user clicks to learn more aboutthe product. In one implementation, the ATMOS may generate synchronizedadvertisement to a user based on the ad tag by providing a pop-up staticad 338 b, e.g., “Do you like Penny's sunglasses?” (e.g., see 720 b inFIG. 7F). In another implementation, the ATMOS may generate aninteractive ad 338 b including a tagged screenshot of the TV programcontaining the placed product (e.g., see 750 c in FIG. 7G).

In another embodiment, the ATMOS server 220 may generate questions 338 bsynchronized and/or related to the TV program ads 343 a, which may bedevised by the ATMOS based on the media content the user has viewed, theadvertisement the user has viewed, and/or the like. For example, if thechannel the user has been watching recently has played advertisement of“Audi,” the ATMOS may prompt a question to the user such as “which brandautomobile would you prefer?” to determine the advertisement effects. Inanother example, the user 233 a may receive real-time informationsynchronized with a product placement embedded in the TV program. Forexample, when a user is watching a TV show, he may receive a promptquestion 338 b related to an embedded advertisement in a scene of the TVshow, e.g., “Do you want to learn more about Penny's sunglasses?” In afurther implementation, the ATMOS may redirect the user to a URL“www.buythings.com/XYZ-designer/spring2000” if the user clicks to learnmore about the product.

For example, in one implementation, the ATMOS server 220 may provide aHTTPS PUT message including the questionnaire 338 b in the form of dataformatted according to the XML. Below is an example HTTP(S) PUT messageincluding an XML-formatted questions 338 b to provide to the user 233 a:

PUT /question.php HTTP/1.1 Host: www.TV.com Content-Type:Application/XML Content-Length: 718 <?XML version = “1.0” encoding =“UTF-8”?> <Question> <QuestionID> Q000123456789 </QuestionID><QuestionName> Automobile Inquiry </QuestionName> <TemplateType> static</TemplateType> <TemplateID> TD0001 </TemplateID> <QuestionDescription><body> ″What automobile brand would you like?″ </body> <Option1> A. BMW<Option1> <Ooption2> B. Audi </Option2> ... </QuestionDescription><QuestionChannel> CBS </QuestionChannel> <QuestionMediaID> 123456789</QuestionMediaID> <QuestionGenre> Product Ad </QuestionGenre><QuestionPromptTime> 50′56″ </QuestionPromptTime> ... </Question>

In one embodiment, upon receiving questions and/or ads at the mobiledevice, the user may submit a response 339 to the ATMOS server, e.g., ananswer to the question, a click on the provided ad URL, and/or the like.In another implementation, upon viewing an embedded advertisement whilewatching a TV program, the user 233 a may desire to learn more orpurchase the product, and submit a request of purchase 365 a (e.g., byclicking on “Buy it Now” 750 c in FIG. 7G) to the ATMOS server 220. TheATMOS 220 may forward the purchase request 365 b to a merchant website280, and redirect the user to view the merchant site to obtain moreinformation of the interested product. In one implementation, the ATMOSmay log the user question responses, indication of interests, purchasetransaction, and/or the like at the database 219 to indicate ad effects.

For example, in one implementation, the ATMOS server 220 may provide aHTTPS PUT message including the questionnaire response 339 b, purchaserequest 365 a in the form of data formatted according to the XML. Belowis an example HTTP(S) PUT message including an XML-formatted questionsresponses/purchasing request to provide to the database 219:

PUT /responses.php HTTP/1.1 Host: www.ATMOS.com Content-Type:Application/XML Content-Length: 718 <?XML version = “1.0” encoding =“UTF-8”?> <Response> <ResponseID> R000123456789 </ResponseID><Timestamp> 19:00:00 </Timestamp> <Date> 09-09-xxxx </Date> <QuestionID>Q000123456789 </QuestionID> <QuestionName> Automobile Inquiry</QuestionName> <QuestionDescription> <body> ″What automobile brandwould you like?″ </body> <Option1> A. BMW <Option1> <Ooption2> B. Audi</Option2> ... </QuestionDescription> ... <QuestionResponse> <UserID>JS001 <UserID> <UserName> John Smith </UserName <DeviceID> JSiPhone0002</DeviceID> ... <Response> B </Response> </QuestionResponse> ...</Response>

In further implementations, the ATMOS may populate social media feeds ofthe user's questionnaire responses/purchase information 365 b to asocial media platform 250. In another implementation, the user may sharepurchase information 238 to the social media 250. For example, theuser's Facebook news feeds may comprise a message “XXX participated in asurvey. See her response” (e.g., see 731 c in FIG. 7C). The user mayalso obtain his friends' ATMOS activities, including participation insurveys, purchases 349, etc., from social media news feed.

FIG. 3B provides a logic flow diagram illustrating generating a surveyquestion within embodiments of the ATMOS. Within embodiments, upon usersubmitting a channel selection 305, the ATMOS server may check programtable to determine what's on air on the selected channel 306, andretrieve a program table to obtain ads tagged in the selected TV program307.

In one implementation, the ATMOS may parse commercial ad information onthe selected channel 308 a, to extract key terms. For example, in oneimplementation, the ATMOS may retrieve the advertised product brandname, product name, category, etc. In one implementation, the ATMOS mayquery on a questionnaire database based on the parsed ad key terms 310.For example, for an “Audi” commercial, the ATMOS may parse key terms as“Audi,” “car,” “automobile,” and select and generate pop-up questionsrelated to such key terms to the user 312.

In another implementation, the ATMOS may incorporate a variety of usermedia content exposure data to generate media content based surveyquestions 308 b. For example, ATMOS may incorporate mobile ads exposuredata (e.g., user web visits, ATMOS generated mobile ads, etc.), userapplication status (e.g., browsing history, Internet gaming content,etc.), social content (e.g., social pages, social ads, friends'recommendations, user' likes, etc.) 305 b, and/or the like. In oneimplementation, the ATMOS may receive the various user content exposuredata from a ATMOS client component instantiated on the user mobiledevice, e.g., an iPhone app, etc. In another implementation, the ATMOSmay receive mobile data from a mobile meter, a proxy server, a TVmetering system, and/or the like.

In one implementation, the ATMOS may generate synchronized pop-up surveyquestions to the user. For example, in one implementation, the ATMOS mayanalyze the ad tags prior to the TV program on air, and prepare pop-upquestions associated with each ad tag. The generated pop-up questionsmay be sent to the user according to the timetable of the ad tags. Inanother implementation, the ATMOS may retrieve the user's viewinghistory, e.g., the TV programs the user has recently watched, etc., anddetermine the ads associated with the TV programs the user has watchedto generate non-synchronized pop-up survey questions.

Upon receiving the pop-up survey question 313, the user may elect tosubmit a response 315, which may indicate ad effects. In furtherimplementations, the survey questions may be generated based onadvertisement the user has exposed to (e.g., via cross-channel admeasurement, as further illustrated in FIG. 7E), social media contents,and/or the like.

FIG. 3C provides a logic flow diagram illustrating generating productplacement ads within embodiments of the ATMOS. In one implementation,continuing on with retrieving ad tags in the TV program on air on theuser selected channel 307, the ATMOS server may query on what ads areavailable 328 in an ad database. For example, the ATMOS may obtain alist of ad tags and it associated types, whether a screen shot forproduct placement is available, etc. In one implementation, if there isa synchronized ad tag 330, the ATMOS may prepare synchronized adgeneration prior to the timestamp of the ad tag 333. In oneimplementation, ATMOS may determine an ad type 335, e.g., whether it isstatic ad or interactive ad. In one implementation, if it is a staticad, the ATMOS may select a static ad template and populate the productinformation into the template, and provide a textual pop-up ad at asynchronized time 340 to the user. In another implementation, if the adtag indicates an interactive ad is available, ATMOS may retrieve atagged screenshot associated with the interactive ad tag. For anotherimplementation, the ATMOS may generate a video frame grab comprising theproduct placement tags 338 via video frame grabbing software (e.g.,Windows Media Player, Quicktime Player, etc.). In one implementation,the ATMOS may retrieve an interactive ad template and populate the videoframe into the template to provide the interactive ad (e.g., see FIG.7G) at a synchronized time 342 to the user.

In one implementation, if the TV program at the user submitted channelcontains no synchronized ad tags 330, the ATMOS may elect not to sendads/questions to the user. In another implementation, the ATMOS mayretrieve user's recent viewing history (e.g., the past week, etc.), andgenerate a non-synchronized ad/survey question to the user based on theuser's recently viewed TV programs. In further implementations, theATMOS may re-send ads that were synchronized with one of user's recentlyviewed TV programs to the user.

In further implementations, the synchronized product placement ads maybe applied to in-game ads in a similar manner. For example, the ATMOSmay determine a user is engaging in a gaming application via thereceived device application event (e.g., 241 in FIG. 2B), and deliver aninteractive advertisement of related virtual goods, e.g., gaming points,widget, etc., to the user mobile device.

FIG. 3D shows a logic flow diagram illustrating ad synchronizationquestion message processing (e.g., 289 c) within embodiments of theATMOS. Within embodiments, upon receiving a message from a user anddetermining the type of the message as an ad/question response 289 c,the ATMOS may determine whether the received message comprise a responseto a survey question 362.

In another implementation, if the received message comprises responsesto prompt questions 362, the ATMOS may determine a classification of thequestion 365, e.g., a response to survey, a response to embeddedadvertisement, and/or the like. In another implementation, the questionresponses may be classified by the products, e.g., automobiles,apparels, electronics, and/or the like. In one implementation, the ATMOSmay extract a questionnaire ID and/or a survey ID 366 from the receiveduser response, and store the questionnaire results 368 associatedmatched with the questionnaire based on an ID query.

In a further implementation, the user who responds to questionnaires maybe credited for a reward. For example, after obtaining and storingquestionnaire results, the ATMOS may determine rewards for the user 370,e.g., five ATMOS points for each question answered, etc., and credit thepoints to the user's ATMOS account 372. In another implementations, therewards may comprise virtual currency (e.g., store points, gamingpoints, etc.), coupons, discounts, and/or the like, sponsored by anadvertising merchant.

In another implementation, when the ATMOS determines the response doesnot comprise a response to a survey question at 362, the ATMOS maydetermine whether it comprises an interactive activity indication 376.For example, a user may submit a rating of the product with theinteractive ad, click on the interactive ad, and/or the like. In oneimplementation, if the user submits a purchase request 378, the ATMOSmay provide a merchant URL, and/or direct the user to a merchant page toproceed with purchase transaction 377 a. The ATMOS may log useractivities associated with the product placement advertisement 377 b,e.g., with an ad ID, etc.

In one implementation, the ATMOS may aggregate data analysis resultsfrom all different types of messages received from the user and runaggregate analytics 375 for ad effects. FIG. 3E provides a logic flowdiagram illustrating ad delivery/effects analysis within embodiments ofthe ATMOS. For example, in one implementation, ATMOS may obtain acorrelation of an advertisement and user perception of the product,based on responses to the survey question (e.g., if a user selects“Audi” in an automobile survey, after viewing an “Audi” advertisement,etc.). In further implementation, ATMOS may generate an ad effect scorefor each advertisement.

In one implementation, ATMOS may retrieve an advertisement and determinean ad classification 380, e.g., a category of the advertised product(e.g., apparel, accessories, automobile, electronics, etc.). Forexample, for an “Audi” advertisement, the ATMOS may query for storedquestions (e.g., 368 in FIG. 3D) results with the ad classification“automobile” 381. If such survey responses are available 382, the ATMOSmay query the retrieved survey responses for mentions of the product383, e.g., on a brand name “Audi,” on a make and model of the “Audi”automobile, etc. For each response, the ATMOS may determine a relevanceof the question and question results 382. For example, the ATMOS mayretrieve the corresponding question with the question results based on aquestion ID (e.g., stored at 366 in FIG. 3E), and perform text analyticsto determine a relevance level of the question to the brand name product“Audi” automobile (e.g., based on whether the question contains keyterms such as “preference,” “car purchase,” etc.). In oneimplementation, the ATMOS may determine an ad effect weight value forthe response 385. For example, the ATMOS may perform text analytics ofthe question and questions results, and if the textual question/questionresults contain key terms such as “car purchase” and “Audi,” suchresponse may be accorded with a high weight value. In oneimplementation, the weighting value determination at 385 may be based ona pre-stored weight evaluation table, e.g., 0.5 for submitting aresponse of “Audi,” 0.8 for clicking on a merchant site, 50.0 fortransacting a sale on an “Audi” automobile,” etc., and calculate animpact score of the advertisement based on a group of users (e.g., see1019I at FIG. 10). The ATMOS may generate an ad effect sub-score basedon analytics of user survey responses 386 a, e.g., taking a weightedsum, etc.

In another implementation, the ATMOS may query for stored useractivities (e.g., stored at 377 b in FIG. 3D) in response to an ad(e.g., purchasing request, click for more information, etc.) base on anad ID 382. If such activities are available 387, the ATMOS may determinea type of the activity 388, e.g., clicks on the ad for more information,clicks on a provided merchant URL, user rating of the advertisedproduct, clicks on “Buy It Now,” closing the ad without browsing, and/orthe like. Based on the activity type, the ATMOS may associate a weightvalue for the activity 389, and generate an ad effect sub-score based onanalytics of user activities 386 b, e.g., taking a weighted sum, etc.For example, a click on a merchant URL may be accorded with a highpositive weight value; and a prompt window close of the ad may beaccorded with a low or zero weight value, and/or the like. In oneimplementation, the ATMOS may generate an indication of ad effects 390based on an integrated ad effects score (e.g., taking a sum of thesubscores from 386 a and 387 b, etc.).

In one implementation, ATMOS may compare the ad effects score of thesame ad on different media channel, to determine efficiency of the adplacement. For example, if the same “Audi” ad has a higher impact scoreon channel “ESPN” than “Disney,” it may provide heuristics to themerchant that such advertisement is more efficient on “ESPN.” In furtherimplementations, ATMOS may determine efficiency of the time and the TVprogram to place the advertisement based on the ad effects score.

FIG. 4A provides a block diagram illustrating a ATMOS client component401 within embodiments of the ATMOS. Within embodiments, a ATMOScomponent 401 may contain a number of sub-components and/or data stores.A ATMOS client controller 405 may serve a central role in someembodiments of ATMOS operation, serving to orchestrate the reception,generation, and distribution of data and/or instructions to, from andbetween client mobile device(s) and/or the server via ATMOS componentsand in some instances mediating communications with external entitiesand systems.

In one embodiment, the ATMOS controller 405 and/or the differentcomponents may be instantiated on a user mobile device, e.g., an AppleiPhone, etc. In an alternative embodiment, the controller may be housedseparately from other components and/or databases within the ATMOSsystem, while in another embodiment, some or all of the other modulesand/or databases may be housed within and/or configured as part of theATMOS controller. Further detail regarding implementations of ATMOScontroller operations, modules, and databases is provided below.

In one embodiment, the ATMOS controller 405 may be coupled to one ormore interface components and/or modules. In one embodiment, the ATMOSController may be coupled to a user interface (UI) 410. The userinterface 410 may be configured to receive user inputs and displayapplication states and/or other outputs. The UI may, for example, allowa user to adjust ATMOS system settings, select communication methodsand/or protocols, manually enter texts, engage mobile device applicationfeatures, and/or the like. In one implementation, the user interface 410may include, but not limited to devices such as, keyboard(s), mouse,stylus(es), touch screen(s), digital display(s), and/or the like. Inanother implementation, the user questionnaire component 430 may provideuser survey questions and receive user responses via the user interface410.

In one implementation, the ATMOS Controller 405 may further be coupledto a sensor module 420, configured to interface with and/or processsignals from sensor input/output (I/O) components 425. The sensor I/Ocomponents 425 may be configured to obtain information of environmentalconditions, and/or the like to generate atmospherics data that may bereceived and/or processed by other ATMOS components. A wide variety ofdifferent sensors may be compatible with ATMOS operation and may beintegrated with sensor I/O components 425, such as but not limited to acamera, an audio recorder, a GPS component, and/or the like, configuredto capture video clips/photos of what is playing on the TV and/orwhether the user is watching the program, audio recording clipsindicative of what is playing on the TV, GPS information indicative ofthe user's location, and/or the like. In one implementation, the MediaListen-In Component 440 may configure, aggregate and packetizeatmospherics data captured by the sensor module component 420 in a dataformat suitable for data transmission via the sensor I/O 425. In afurther implementation, the Media Listen-In Component 440 may processand analyze the obtained atmospherics data, e.g., a photo captured bythe mobile device, etc., to identify whether the user is watching,and/or how many individuals are watching from the photo, via imageprocessing. For example, in one embodiment, the iPhone SDK toolkitand/or runtime libraries may be installed and/or used to perform suchimage processing.

In one embodiment, the ATMOS Controller 405 may further be coupled to acommunications module 430, configured to interface with and/or processdata transmission from communications I/O components 435. Thecommunications I/O components 435 may comprise components facilitatingtransmission of electronic communications via a variety of differentcommunication protocols and/or formats as coordinated with and/or by thecommunications module 430. Communication I/O components 440 may, forexample, contain ports, slots, antennas, amplifiers, and/or the like tofacilitate transmission of TV program listing information, usersubmission of channel selection, user responses to survey questions,and/or the like, via any of the aforementioned methods. Communicationprotocols and/or formats for which the communications module 230 and/orcommunications IO components 435 may be compatible may include, but arenot limited to, GSM, GPRS, W-CDMA, CDMA, CDMA2000, HSDPA, Ethernet,WiFi, Bluetooth, USB, and/or the like. In various implementations, thecommunication I/O 435 may, for example, serve to configure data intoapplication, transport, network, media access control, and/or physicallayer formats in accordance with a network transmission protocol, suchas, but not limited to FTP, TCP/IP, SMTP, Short Message Peer-to-Peer(SMPP) and/or the like. The communications module 430 and communicationsI/O 435 may further be configurable to implement and/or translateWireless Application Protocol (WAP), VoIP and/or the like data formatsand/or protocols. The communications I/O 435 may further house one ormore ports, jacks, antennas, and/or the like to facilitate wired and/orwireless communications with and/or within the ATMOS system. Forexample, the communication I/O 432 may be extended by a plug-inaccessory as shown at 120 in FIG. 1.

Numerous data transfer protocols may also be employed as ATMOSconnections, for example, TCP/IP and/or higher protocols such as HTTPpost, FTP put commands, and/or the like. In one implementation, thecommunications module 430 may comprise web server software equipped toconfigure application state data for publication on the World Wide Web.Published application state data may, in one implementation, berepresented as an integrated video, animation, rich internetapplication, and/or the like configured in accordance with a multimediaplug-in such as Adobe Flash. In another implementation, thecommunications module 430 may comprise remote access software, such asCitrix, Virtual Network Computing (VNC), and/or the like equipped toconfigure user application (e.g., a user mobile device). In anotherimplementation, the communications module 430 may transmit TV programlisting information to the real time TV remote control component 415,which may in turn receives user channel selection form the userinterface 410.

In further implementations, the ATMOS 405 may be configured tocommunicate with a content based embedded advertising component 420,media content questionnaire synchronization component 435, and/or thelike (as further discussed in FIGS. 3B and 3C).

FIG. 4B provides a combined logic and data flow diagram illustratingATMOS client and server interactions within embodiment of the ATMOS. Inone embodiment, the TV remote control component 415 may submit a channelselection event 416 to the ATMOS server 450, which may in turn query amedia program database 419 for TV program information. For example, inone implementation, the ATMOS server 450 may form a first query 417 on amedia program table 419 a based on the user's channel selection, todetermine the TV program the user is watching and send such programinformation 418 back to the user; and a second query on an Ad Tags table419 b to determine what advertisement and embedded product placementadvertisement the user may received during the TV program streaming. Thequeried results, including the program information and ad information,may be fed into an Ad synchronization component 455 at the ATMOS server450, which may generate ad synchronization popup questions 421 based onthe ad the user may be watching.

In one implementation, the generated ad synchronization questions 421may be received and provided to the user via a user interface generatedby the user questionnaire component 430 on the user's mobile device,which may in turn provide user's response events 422 to an Ad effectengine component 460 at the MR-PLATFORM server 450 to analyzeadvertisement effects.

In one embodiment, the media listen-in component 440 may collect andaggregate atmospherics data 423, e.g., video recording clips, audiorecording clips, photo streams, GPS information, and/or the like, to amedia viewing statistics analysis component 465, which may analyze themedia viewing data, and determine the audience reception rate of a TVprogram and/or advertisement.

In a further implementation, the social media connection component 445may generate and transmit social media post 448 indicating the user'sviewing status to a social media database 469, and may optionally sendthe social media post to the ATMOS 450 as well. In one implementation,the ATMOS server 450 may obtain the user's social media status updatesinformation, including friends' recommendations, comments, and/or thelike via an API call 471 to the social media database 469. In anotherimplementation, the ATMOS server 450 may redirect a user to the socialmedia website 472 from the user's instantiated ATMOS client component toengage in social media activities. For example, the user may click on asocial media link via the ATMOS client component user interface and beredirected to the social media page.

FIG. 4C provides a block diagram illustrating an example infrastructureof a ATMOS table top unit component within implementations of the ATMOS.Within implementations, the table top device 473 may be a standalonedevice that may be placed on a desk, wherein the user may be requestedto place it to face the screen of a TV set or the TV STB. In oneimplementation, the table top device 473 may communicate with a usermobile device, a laptop computer, a desktop computer, and/or the like,via a Bluetooth transmitter/receiver interface 472-474. In oneimplementation via Bluetooth connection 476. a user may download andinstall a remote control mobile application 471. In anotherimplementation, the table top device 473 may communicate with a TV set483, a set top box 486, DVR equipment, and/or the like, via infraredtransmitter/receiver interfaces 475, 485/487 via infrared connection481. In one implementation, the table top device 473 may comprise arechargeable battery 478 for power supplies from charging inputs 480.

The table top device 473 may facilitate mobile remote control to operatein a similar manner as the ATMOS accessory 1120 in FIG. 1B. Instead ofbeing plugged into a user mobile device, the table top device 473 maycommunicate with a user device running the remote mobile application 471via wireless connections. For example, the table top device may beoperated for remote TV channel control, collecting atmospherics data foraudience monitoring, and/or the like. In further implementations, thetable top device may be positioned so that the table top device maycapture images from the screen of the TV set.

In one implementation, the table top device may be configured toperiodically monitor audio contents, video contents, etc., in theatmosphere, with or without having user input of a channel selection.For example, the table top device may record an audio/video clip ofmedia program being played, and send such audio/video clip (or generatea signature/hash based on sampling of the recorded audio/video mediacontent) to the ATMOS server, which may in turn determine what the useris watching. Further implementations are discussed in FIG. 2E.

FIGS. 5A-5B provide combined data flow and logic flow diagramsillustrating data downloads from social media platforms withinimplementations of the ATMOS. Within implementations, ATMOS may obtainsocial media data to measure, and/or influence consumer consumption ofmedia and advertising. For example, ATMOS may track social mediadiscussion to obtain comments, mentions, responses related to an objectunder evaluation (e.g., a brand name product, a TV show, etc.). In oneimplementation, a user may befriend with panelists to “Friend” onFacebook, and/or allow a panelist on Twitter, wherein the panelist maybe a ATMOS personnel, and/or an avatar, etc. The ATMOS may then obtainusers' conversations, wall posts, pages of brands/products the userfollows, products/content the user “like”, social advertising the userhas been exposed to, clicks on pages, etc., to obtain social mediaexposure data. In one implementation, the ATMOS may incorporate commentsof representative panelists, identification of friends across panel,social activities profiling of panel, and/or the like.

In a further implementation, the ATMOS may track social media content(e.g., Facebook and Twitter, etc.) of ATMOS consumers, e.g., users whohas authorized ATMOS to access their social media content. In oneimplementation, the ATMOS may link demographic, behavioral, andattitudinal data from the user's profile information with social mediabehavior. The social media data downloading may be obtained via APIcalls, as discussed in FIGS. 5A-5B.

In a further implementation, the ATMOS may recruit consumers (e.g.,Facebook, Twitter users who have allow ATMOS to access their socialcontent) as ATMOS panelists, e.g., by providing incentive rewards to theusers, etc. In one implementation, the ATMOS may track how social mediamessages propagate throughout a network of social media users (e.g., therecruited panelists, etc.), based on the profiles of the individuals.Such measures of connectivity may be analyzed to measure propagation ofmarketing communications.

In another implementation, each panelist may be associated with a socialmedia specific profile so that their social media activities may betracked to determine whether they are influencers in certain categories,disseminators of information, information consumers, and/or the like.For example, in one implementation, a panelist may be labeled as a “TheBig Bang Theory Fan Wiki,” so that users interested in the show “The BigBang Theory” may follow the panelist to obtain information of the showvia the panelist's posts, comments, and/or the like, related to “The BigBang Theory.”

In one implementation, individual social media profiles may beincorporated to assess advertising targeting performance, enableadvertisers to plan social media campaigns by targeting productinfluencers, and/or the like.

As shown in FIG. 5A, ATMOS server 220 may send an access request to asocial media platform (e.g., Facebook, Twitter, Google+, etc.) foraccess to a user's profile information and social media content, e.g.,news feeds, posted photos, Tweets, comments, activities (“Likes,”“Dislikes,” etc.).

In one implementation, upon receiving the access request 505, the socialmedia 250 may generate and send a user authorization request 510 to theuser 233 a. For example, Facebook and/or Twitter may send an email tothe user 233 a, wherein the email may comprise an authorization linkdirecting the user to a ATMOS authorization page (e.g., as included inthe access request 505). In one implementation, the user 233 a may beaccess the included authorization link via a mobile application UI 511 a(e.g., see FIG. 8A), and/or a web based application UI 511 b (e.g., seeFIG. 8B). As shown in FIG. 8A, the user may click “Allow” to grantpermission of ATMOS to access the user's social media content. Infurther implementation, as shown at 827 in FIG. 8C, the user mayconfigure a scope of information the ATMOS may be allowed to access.

For example, in one implementation, the user's mobile application and/ora web-based application may generate a (Secure) HTTPS PUT authorizationmessage 515 including an application ID and permission indication forthe social media platform in the form of data formatted according to theXML. Below is an example HTTP(S) PUT message including the XML-formattedaccess authorization message provide to Facebook:

PUT /AccessRequest.php HTTP/1.1 Host: 172.16.244.1 Destination:www.Faceobook.com Content-Type: Application/XML Content-Length: 518<?XML version = “1.0” encoding = “UTF-8”?> <Authorization> <AuthID>00001 </AuthID> <Time> 0:00:00 >/Time> <Date> 1-12-XXXX </Date> <UserID>JDOE <UserID> <AppID> Mobile0001 </AppID> <Permission> Yes </Permission><scope> /* Name of Permission elements like User Age, Friends, Messages,Likes etc that ATMOS application may have access to*/ <UserProfile><UserName> Yes </UserName> <UserAge> Yes <UserAge> <UserDOB> Yes</UserDOB> <Work> Yes </Work> <Education> Yes </Education> <Pages> Yes<Pages> <Network> Yes </Network> ... </UserProfile> <Friends> <Number>Yes </Number> <FriendsList> No </FriendsList> ... </Friends><Activities> <PostsonWallSelf> Yes <PostsonWallSelf> <PostsonFriedsWall>No <PostsonFriendsWall> <OthersWallComments> No <OthersWallComments> ...</Activities> ... </scope> .. </Authorization>

In the above example, the authorization message to Facebook may compriseinformation as to the scope of information access, e.g., the user maypermit ATMOS to access the user “JDOE's” Facebook profile including hisname, age, date of birth, work an education information, interestedpages, network, and/or the like; a number of friends of “JDOE,” but maynot access an exact friends list. The user may allow ATMOS to obtain“JDOE's” posts on his own wall, but may not permit access to his postson his friends' wall or friends' comments on his wall, and/or the like.

In one implementation, the social media 250 may pass on the applicationID from the user's mobile or web application and generate a user token518 to ATMOS for confirmation of access permission. In oneimplementation, the ATMOS may determine when data update is needed 520,e.g., the data update from social media may be performed on a periodicbasis (e.g., daily, weekly, etc.). The ATMOS server 220 may generate adata request 522 together with the received user authorization token(e.g., 518) and transmit to the social media platform.

In one implementation, the data request 522 may be sent to the socialmedia platform via a user oAuth protocol, and comprise a ATMOSapplication ID, and/or a user social media ID, and/or the like. Forexample, in one implementation, the ATMOS server 220 may provide a(Secure) HTTPS PUT message including a data request 522 for Facebook inthe form of data formatted according to the XML. Below is an exampleHTTP(S) PUT message including the XML-formatted access request provideto Facebook:

PUT /AccessRequest.php HTTP/1.1 Host: www.ATMOS.com Destination:www.Faceobook.com Content-Type: Application/XML Content-Length: 518<?XML version = “1.0” encoding = “UTF-8”?> <Request> <RequestID> 000001</RequestID> <TimeStamp> 00:00:00 </TimeStamp><Destination>www.Facebook.com </Destination> <UserID> JDOE </UserID><ClientID> ccccccc </ClientID> /* ATMOS Mobile Remote Application Id orweb-based ATMOS Panel management App Id obtained after registering theapplication with Facebook*/ <RedirectURL>www.ATMOS.com/user?8989898988.com </RedirectURL> /* ATMOS applicationURL where the user may be redirected after authorization*/ <Content> /*Name of Permission elements like User Age, Friends, Messages, Likes etcthat ATMOS application may have access to*/ <UserProfile> <UserName> Yes</UserName> <UserAge> Yes <UserAge> <UserDOB> Yes </UserDOB> <Work> Yes</Work> <Education> Yes </Education> <Pages> Yes <Pages> <Network> Yes</Network> ... </UserProfile> <Friends> <Number> Yes </Number><FriendsList> No </FriendsList> ... </Friends> <Activities><PostsonWallSelf> Yes <PostsonWallSelf> <PostsonFriedsWall> No<PostsonFriendsWall> <OthersWallComments> No <OthersWallComments> ...</Activities> ... </Content> ... </Request>

In the above example, the data request generated by ATMOS to Facebookmay comprise a user ID “JDOE” indicating the request is directed toFacebook information of the user “JDOE”; a client ID indicating theapplication (e.g., the ATMOS mobile application ID, etc.) indicating asource of the request; and a URL link which may be provided to the userfor authorization, e.g., a link that requests the user to click a “OK”or “Cancel” button on the page to authorize or deny ATMOS to gain accessto the user's Facebook content. The access request may further compriseinformation as to the scope of information access, e.g., ATMOS mayrequest to access the user “JDOE's” Facebook profile including his name,age, date of birth, work an education information, interested pages,network, and/or the like. The ATMOS may also request to obtaininformation of a number of friends of “JDOE,” but may not request toaccess an exact friends list. The ATMOS may further request to obtain“JDOE's” posts on his own wall, but may not request to obtain his postson his friends' wall or friends' comments on his wall, and/or the like.

For another example, in one implementation, the ATMOS server 220 mayprovide a (Secure) HTTPS PUT message including a data request 522 forTwitter server in the form of data formatted according to the XML. Belowis an example HTTP(S) PUT message including the XML-formatted accessrequest provide to Twitter:

PUT /AccessRequest.php HTTP/1.1 Host: www.ATMOS.com Destination:www.twitter.com Content-Type: Application/XML Content-Length: 518 <?XMLversion = “1.0” encoding = “UTF-8”?> <Request> <RequestID> 000002</RequestID> <TimeStamp> 00:00:01 </TimeStamp> <Destination>www.Twitter.com </Destination> <UserID> JDOE </UserID><oauth_consumer_key> /* The Consumer Key for ATMOS Remote Applicationand/or ATMOS panel management obtained after registering the applicationwith twitter*/ NNNNNNN </oauth_consumer_key> <oauth_signature_method>/*The signature method that the consumer used to sign the request*/email </oauth_signature_method> <oauth_signature> /*The signature asdefined in twitter signing requests> electronic </oauth_signature><oauth_timestamp> /*Time stamp of authoroization*/ 00:00:02</oauth_timestamp> <oauth_nonce> /* Unique string to identify eachrequest*/ .8888ddddd </oauth_nonce> ... </Request>

In the above example of data request to Twitter, the request maycomprise a user signature request. For example, a user may provideelectronic signature by clicking on a link (e.g., at 515, etc.) noting“I hereby provide my signature by pressing this button to allow ATMOSaccess my Twitter content,” etc.

In one implementation, upon verification of the data request byFacebook/Twitter/other social media platform, ATMOS server 220 maydownload social media structured data 525 a and unstructured data 525 b(e.g., see 571 a-b in FIG. 5B) for media analytics.

FIG. 5B shows a block diagram illustrating an example infrastructure mapof ATMOS media analytics within embodiments of the ATMOS. In oneimplementation, ATMOS may provide a media analytics and reportingplatform portal 570 for analyzing TV, Internet, mobile and social mediadata upon client request, e.g., analyzing public feedbacks and commentson a brand name products, TV shows, and/or the like. In oneimplementation, the media measurement and analytics portal platform 570may provide class visualization, self service administration withseamless integration between the different architectural components,e.g., 570 a and 570 b, etc.

In one embodiment, the ATMOS media measurement portal 570 may load datafrom social networks via a HTTP network 578 via API calls, e.g.,Facebook APPI 580 a, Twitter API 580 b, Google+ API 580 c, and othersocial data providers 580 d. The media measurement portal 570 mayprocess the loaded data within different analytics platforms 570 a/bbased on loaded data types, e.g., structured data 571 a or unstructureddata 571 b.

For example, in one implementation, structured data 571 a may be alreadystored in a structured format when loaded from the data source, such as,but not limited to user TV channel selection indication with timestamp,web displaying content with timestamp, social media user profileinformation, a number of user's social connections (time stamped), aposted photo on social media platform with timestamp, and/or the like.For example, an exemplary XML record of structured Facebook user profiledata 571 a downloaded from Facebook may take a form similar to thefollowing:

PUT /userprofile.php HTTP/1.1 Host: www.ATMOS.com Content-Type:Application/XML Content-Length: 718 <?XML version = “1.0” encoding =“UTF-8”?> <User> <UserID> JDoe </UserID> <UserFirstName> John<//UserFirstName> <UserMI> Null </UserMI> <UserLastName> Doe</UserLastName> <UserProfilePhoto> Me.JPG </UserProfilePhoto><WorkandEducation> <Employers1> <EmployerName> Data Inc.<EmpoyerName><StartTime> 2006 </StartTime> <EndTime> present </EndTime> ...</Employers> <College> Good University </College> <Classyear> 2000</ClassYear> ... </WorkandEducation> ... <Interests> <Interests1>Graduate Program </Interests1> <Interests2> Cool Cars </Interests2></Interests> ... <Friends> <Number> 82 </Number> <List> Lisa Smith; ....</List> ... </Friends> ... <User>

In one implementation, the analytics platform for processing structureddata may store the structured data, such as TV channel selection,mobile/web content data, social network user profile data, etc. in adatabase 575 a.

For another example, unstructured data 571 b may comprise raw textdownloaded from social media platform, e.g., friends' comments fromFacebook, original Tweets, etc. In one implementation, ATMOS socialanalytics platform 570 b may perform data mining on unstructured data571 b to measure user feedbacks of a brand name product, TV program,etc. For example, a query may be performed on the unstructured data 571b to determine how many mentions of “The Big Bang Theory” are posted byFacebook users.

For example, in one implementation, an exemplary XML record ofunstructured Facebook user message 571 b downloaded from Facebook maytake a form similar to the following:

PUT /usercomments.php HTTP/1.1 Host: www.ATMOS.com Content-Type:Application/XML Content-Length: 718 <?XML version = “1.0” encoding =“UTF-8”?> <SocialMessage> <UserID> JDoe </UserID> <UserFirstName> John<//UserFirstName> <UserMI> Null </UserMI> <UserLastName> Doe</UserLastName> <MessageID> 00001 </MessageID> <Time> 19:00:45 </Time><Date> 09-09-2011 </Date> <SocialType> Like </SocialType> <Object><Type> Facebook Page </Type> <Name> Big Bang Theory </Name> <Category>TV </Category> <Genre> Comedy </Genre> ... </Object> <ActivityTime>14:33:56 </ActivityTime> <ActivityDate> 09-09-2011 </ActivityDate> ...</User>

For further implementations, the unstructured data 571 b may comprise asocial post, a social media check-in status, social mentions, and/or thelike.

In one implementation, the ATMOS structured data analytics platformcomprises a variety of processing components, such as but not limited touser permission component for social media access 573 a, TV/mobile/webmeasurement reports component 573 b, media analytics engine 573 c,organization/user account management component 573 d, single source dataextraction, transformation and load (ETL) component 573 e, and/or thelike. Within implementations, the user permission component may presentpanel users with a set of user interface screens requesting them toprovide permission for ATMOS to access their social content. Forexample, ATMOS may provide incentives of promotional rewards, points,coupons and/or the like to users during questionnaire distribution(e.g., ATMOS mobile questionnaires 238 a, etc) to allow ATMOS access totheir social content.

In one implementation, if the user agrees to provide the access to theirsocial content, the permission component 573 a may get the authorizationtoken from the respective social platforms (Facebook, twitter etc) asper the authorization protocol and persist the token in a repository 575b. The user permission component 573 a may pass the user token and otherapplication authorization details to social analytics platform so thatthe user's social content can be extracted at a scheduled frequency. Forexample, ATMOS may periodically, constantly and/or intermittently loadsocial content data from the social media platforms via API calls onceauthentication is established. Further implementations of userpermission flows are discussed in FIGS. 5A-5B.

In one implementation, the media analytics engine 573 c may analyze theloaded structured data 573 c, e.g., per user profile, per media type,etc. In one implementation, the organization/user management 573 d maycreate and manage user accounts with the ATMOS. In furtherimplementations, the TV/mobile/web measurement reports component 573 bmay generate media measurement reports (e.g., including audienceratings, ad effectiveness, etc.) based on structured media data, such asaudience TV channel selections, mobile/web browsing activities, etc.

In one implementation, the ATMOS unstructured data analytics platform570 b may have a rule to define the specific fields for which thecontent needs to be extracted for a given user (e.g. user demographics,no of friends, no of messages in a given duration, actual text formessage and comments etc). Based on the authentication token the socialmedia analytics platform 570 b may query the social platform for thecontent. In one implementation, the social analytics platform 570 b mayschedule the query tasks and persist structured and unstructured contentthat is extracted.

In one implementation, the ATMOS social analytics platform 570 b maycomprise a variety of processing components, such as but not limited to,social media measurement reports component 583 a, taxonomy managementconsole 583 b, text analytics engine 583 c, social media adapters 583 d,social analytics platform API 583 e, and/or the like. The socialanalytics platform API 583 e may exchange data via API calls 587 withthe single source ETL process component 573 e, such as userauthorization tokens, and/or the like.

In one implementation, the taxonomy management console 583 b may definetaxonomy tags and taxonomy logic rules. For example, the taxonomy may bedefined at three different levels: a standard taxonomy specific to anindustry and business function as provided by the platform vendor (e.g.,tagging a unstructured data record by industry, etc.); taxonomy definedby analyst and subject matter experts (e.g., ATMOS analytics definedtaxonomy rules, etc.); and/or taxonomy defined by clients, and/or thelike. For example, for a Tweet “Good adaption of the Southern Vampireseries. Love the CBS actors/actresses. Expecting the new season” (e.g.,185 in FIG. 1D), the text analytics engine 583 c may apply a taxonomyrule to tag it by a hierarchy of tags “TV show->CBS->The Big BangTheory->Positive Feedback.” For another example, the client (e.g., CBS,etc.) may desire to categorize the commented target of the Tweet, andtherefore the taxonomy rule may comprise an additional sub-category“actor/actresses,” and/or the like. In one implementations, suchtaxonomy rules may be stored in a repository 585 a, and the originalsocial text data and social graph, tagged social data may be stored at585 b. Further implementations and applications of the taxonomy rulesare discussed in FIGS. 5E-5F.

In one implementation, the social media measurement reports 583 a mayprovide a user feedback measurement report to a user via UI integrationand data visualization. Within implementations, the reporting portal 583a may leverage the web based visual components (e.g. word cloud, trafficlight components etc) provided by the social media analytics platform570 for analysis that deliver insights purely on social media data. Infurther implementations, for insights generated on combined data setfrom social and other data sources (e.g., via data mesh-up 572 betweenthe structured data analytics platform 570 a and unstructured dataanalytics platform 570 b, etc.) the taxonomy management console 583 bmay be integrated to an integrated reporting portal to ensure theclients have the self service capability of defining the taxonomy andbuilding the reports. For example, single sign on and UI widget meshup572 may be adopted between the two platforms 570 a and 570 b forintegration of the two.

In one implementation, the ATMOS media measurement portal 570 maycombine structured social media data with other data sources to generatecross media insights. The social media analytics platform 570 b may havethe API to extract data for predefined metrics and attributes (e.g.,taxonomy logics, data tags, etc.). The metrics and attributes may bepredefined in the social media analytics tool to compute from thestructured and unstructured content extracted from the social mediaplatforms.

In one implementation, the ATMOS media measurement portal 570 may usedifferent platforms for social media reporting and structured datareporting, and have a tight integration at the data and UI layers ofboth the platforms. Additional implementations of the ATMOS mediameasurement portal 570 may comprise: out of the box connectors forsocial media platforms like Facebook, Twitter, Google+, etc (e.g., at583 e, etc.); web based taxonomy creation and management userinterfaces; industry specific prebuilt taxonomies (e.g., at 583 b,etc.); text analytics engine with sentiment analysis with learningalgorithms (e.g., at 583 c, etc.); ability to define fields for whichsocial content needs to be extracted (e.g., at 583 c, etc.); data APIcalls to extract large data set in and out of the platform in real timeand/or in batch mode (e.g., at 583 d, etc.); data API calls for UIwidget integration to create mesh-up (e.g., at 572, etc.); multipletenant support to ensure taxonomies defined at organization anddepartment level (e.g., at 583 b, etc.); single sign on support withactive directory service interfaces (ADSI) and other light weightdirectory access protocol (LDAP) providers (e.g., at 572, 573 a, etc.);components to generate insights from large historical data (e.g., overtwo terabytes of data); components to schedule data extraction jobs fromsocial media sites (e.g., at 583 e, etc.); components to support to hostthe service on a cloud, and/or the like.

FIG. 5C provide a logic flow diagram of user authorization for socialmedia access and social message (e.g., 289 d at FIG. 2C) processingwithin implementations of the ATMOS. In one embodiment, upon receiving asocial message from a user, the ATMOS may determine whether the user,and/or the ATMOS is authorized to send social messages to the user'ssocial media platform.

In one implementation, the user 233 a may allow ATMOS platform to accessto their social network. For example, in one implementation, the user233 a may sign up for ATMOS permission via a ATMOS mobile application511 a from a mobile device (e.g., an Apple iPhone, an Android, etc.).For another example, the user 233 a may visit a ATMOS social data panelmanagement website 511 b (e.g., as illustrated in one example in FIG.8B). In each case, ATMOS may provide a sign up link to a social mediaplatform to the user. Upon user clicking the link, the ATMOS may verifywhether the user previously authorized 532 a/b ATMOS to access theirFacebook or Twitter information on their behalf and their previousauthorization token (e.g., 522 in FIG. 5A) is not expired.

In another implementation, the ATMOS may receive user attempts to log inand determine whether the user is authorized 532 a/b to grant socialmedia data access permission for ATMOS. For example, when a user hasreceived an email from a social media platform (e.g., Facebook, Twitter,etc.) comprising a ATMOS link for authentication and the user clicks thelink to proceed to grant permissions for ATMOS, the user may deemedauthorized to configure social media permissions via ATMOS. In anotherimplementation, when ATMOS determines the user is not authorized, ATMOSmay redirect the user to a social media page 533 a/b with a ATMOSapplication ID and a permission attribute request, e.g., as discussed at515 in FIG. 5A.

In one implementation, the ATMOS may determine whether access isapproved by the user, e.g., the user may select “Yes” or “Cancel” toindicate permission decision for ATMOS to access the user's social mediacontent when the social media platform sent an email notificationindicating the access request from ATMOS, e.g., at 515 in FIG. 5A.

For example, if the previous authorization is not valid or if it is afirst time access then the ATMOS application may use oAuth protocol torequest Facebook or Twitter to provide access to user profile andmessages (e.g., sending an access request 505 as shown in FIG. 5A). Insuch cases, the user may be redirected to Facebook or Twitter web sitewith request to log in and provide access parameters, e.g., see 515 inFIG. 5A, and 827 in FIG. 8C. Facebook or Twitter web site may present alogin and permission screen with option to allow or deny requestedaccess to ATMOS (e.g., see 810 at FIG. 8A).

In one implementation, if the user approves the access request then inthe response Facebook or Twitter may provide an authentication tokenassociated with permission to the data elements (e.g., 518 at FIG. 5A).In one implementation, the authentication tokens may be requested withconstant permission to allow user data access even when the user isneither logged into ATMOS user portal (e.g., the mobile app 511 a or theweb based app 511 b) nor any social media platform. In alternativeimplementations, the authentication token may request timely update andre-authentication from user on a periodic basis, e.g., weekly, monthly,etc. In further implementations, the user may revoke access of ATMOSfrom to their social media account if they wish to opt out.

If the user granted the permission, ATMOS may save the authorizationresponse token provided by the social media platform in a userrepository 535 a/b. The authentication token may be used in allsubsequent requests to social networking sites to access user's dataelements. In another implementation, if the user did not grantpermission, the user may continue with the mobile application or the webbased panel management website 536 a/b.

Upon establishing and/or confirm user authorization for social mediaaccess, ATMOS server may generate social messages to populate to socialmedia platforms via ATMOS 537, e.g., see 275 b in FIG. 2A, 365 b in FIG.3A, etc.

FIG. 5D provides a logic flow diagram illustrating data download fromsocial media within implementations of the ATMOS. Continuing on with 9at FIG. 5A, ATMOS may receive data updates from the social media servers540. The ATMOS may determine a source of the social media data 543,e.g., Facebook, Twitter, Google+, and/or the like, and apply data formatanalytics rules based on the determined data source 545.

For example, if the data record is obtained from Facebook, the dataformat analytics may determine whether it is structured user profileinformation, a user posted photo, unstructured user posting on the wall,others' comments, and/or the like. For another example, if the datarecord is obtained from Twitter, the ATMOS may determine whether itcomprises raw text of a Tweet, and/or the like.

In one implementation, if the received data is structured 538, ATMOS mayparsing the structured data to extract information 552. For example, astructured user profile data record may be parsed per data field, e.g.,user_id, user_name, user_DOB, user_interests, and/or the like. The ATMOSmay generate a data record including user ID/timestamp/geo source, etc.555 and store the data record for the structured data 557 at a database(e.g., see 575 a in FIG. 5B).

In another implementation, if the received data is unstructured 538,e.g., raw texts of Facebook comments, Tweets, etc., ATMOS may feed thedata to a Taxonomy engine for data tagging 560, as further illustratedin FIGS. 6A-6C.

FIGS. 5E-5F provide example data structures of social media data updateswithin embodiments of the ATMOS. For example, the data segments in FIGS.5E-5F show database structure of social data elements associated with auser, wherein various data fields may be linked by a user ID, e.g.,“USER_TBL_ID.”

Within implementations, a ATMOS analytics platform (e.g., 570 at FIG.5C) may extract and use different sets of data elements from a usersocial media profile. For example, in one implementation, as shown inFIGS. 5E-5F, a data record of a Facebook user profile may comprise datafields such as user work information, user education information,television viewing history, interested music, interested books, datafeeds, likes, status message, comments on status message, wall postedcomments, check-in history, and/or the like.

In one implementation, data elements are categorized based upon howfrequently they are updated and ATMOS may accordingly determine thefetch and refresh schedules. For example, the categorization maycomprise users static descriptors, such as user demographic attributeslike date of birth, gender, etc., which may be relatively “constant” and“static, and thus may be determined to scheduled to update every 6months. For another example, the categorization may comprise dynamicdescriptors, such as number of friends, Likes, television viewing, booksand other preferences, which may be updated by a Facebook user morefrequently, and may be scheduled for updates monthly. For anotherexample, the categorization may comprise text messages, status, comment,messages, posts, etc., and may be scheduled to update daily and/or ondemand. In one implementation, ATMOS may specify the requested contentin a data request accordingly, e.g., for user descriptors only, fordynamic descriptor only, for text messages only, and/or any combinationof the above. (e.g., see the “RequestedContent” field in the exampleFacebook data request 522 in FIG. 5A).

FIG. 6A provides a logic flow diagram illustrating social mediaanalytics within embodiments of the ATMOS. In one embodiment, a user maysubmit a request for media analytics 605, e.g., for a brand nameproduct, a TV program, and/or the like. For example, a TV productioncompany may desire to know audience comments about their TV program(e.g., see FIG. 1D). The ATMOS server 220, upon receiving the userrequest, may generate a media analytics management panel user interfacescreen including a list of analytics options (e.g., see FIG. 8C) 610,and the user may submit media analytics parameters 613. For example, asshown in FIG. 58D, the ATMOS management panel may allow a user to selectsocial media source (e.g., Facebook, Twitter, Google+, and/or the like),a time range, feedback activity type, and/or the like.

In one implementation, ATMOS server 220 may download social media dataupdates (e.g., as discussed in FIGS. 5A-5C), and form a query based onthe retrieved social media data 615 based on the user inquiry. Forexample, when the producer CBS would like to know audience feedbacks ofthe show “The Big Bang Theory” (e.g., see FIG. 1D), ATMOS may search fordata related to “The Big Bang Theory” on the retrieved updated socialmedia data.

For example, in one implementation, ATMOS may form a query on thestructured data based on key word “The Big Bang Theory” and/or anycharacter names and/or actors/actress names for the associated profiles,e.g., a Facebook pages, Twitter profiles, and obtain a number offollowers from the structured data. In another example, ATMOS may form aquery based on the key term “The Big Bang Theory” and obtain raw textcomments containing the key term. In one implementation, ATMOS mayperform a progressive search over the raw text (e.g., unstructured data,etc.). For example, ATMOS may search for “The Big Bang Theory,” and thenrefine the search results by “The Big Bang Theory AND CBS,” and refinethe search results by “The Big Bang Theory AND CBS AND Show,” and/or thelike. In one implementation, ATMOS may search the unstructured databased on data tags associated therewith, as further discussed in FIGS.6B-6C.

In one implementation, ATMOS may obtain query results 620, which maycomprise a number of followers on the social media platform, a series ofraw text comments from the social media, and/or the like. The ATMOS maythen determine a presentation format 623 to provide the results to theuser. For example, when the user elects to choose “raw texts” (e.g., see850 in FIG. 8D), the user may view a list of raw text comments 630 (e.g.see 850 a/b in FIG. 8D). In another implementation, ATMOS may select anoutput visualization format and present the visualization of queryresults to the user 625, and the user may view visualized results 633(e.g., see 860 a-c in FIG. 8E).

FIGS. 6B-6C provide logic flow diagrams illustrating example taxonomytagging logics within embodiments of the ATMOS. In one implementation,as discussed in FIG. 5C, unstructured data (e.g., raw texts of Facebookcomments, Tweets, and/or the like) may be parsed and tagged withcategory tags by a taxonomy engine (e.g., see 583 b-383 c in FIG. 5C).

In one implementation, ATMOS may apply taxonomy model logics, which maycomprise any of a first logical section associated with the semanticcategorization of keywords and a second logical section associated withthe sentiment keywords. In one implementation, the taxonomy may assignweighted scores to the logical tagging in conjunction with the semantictext.

For example, in one implementation, the semantic categorization logicmay be hierarchical and specific to a domain to maintain simplicity ofin model management and run time executions, e.g. a specific taxonomymodel for TV shows, a specific taxonomy model for advertisement andbrand mentions, a taxonomy model for any combination of the above,and/or the like.

FIG. 6B shows an example TV shows semantic categorization model withinimplementations of the ATMOS. In one implementation, unstructured datarelated to TV shows will be tagged with “TV shows” 640 as a level 0 tag,and progressively be tagged with level 1, 2, 5, etc based on genre 641,show names 642, show network 643 a, show episodes 643 b, show castmember name 643 c, show character names 643 d, show episode names 643 e,other keyword texts in the descriptor 643 f and/or the like. Forexample, the example Tweet 185 in FIG. 1D, “The Big Bang Theory is agood adaption of the Southern Vampires series. Love the CBSactors/actresses. Expecting the new season,” may be tagged as “TVshow”->“Soap” (genre)->“Comedy” (sub-genre)->“The Big Bang Theory” (showname), “CBS” (network), “new season” (episode), “geeks” (key words indescriptor), and/or the like.

FIG. 6C shows an example advertisement brand awareness model withinimplementations of the ATMOS. In one implementation, unstructured datarelated to an advertised and/or brand product may be tagged with “allcategories” 645 as a level 0 tag, and progressively be tagged with level1, 2, 5, etc based product categories 646, product brand 647, ad contentkey words 648 a, brand name 648 b, brand name+ad content key words 648c, product mentions 648 d, product name/type/model 648 e, key word textin descriptor 648 f, and/or the like. For example, in oneimplementation, a Facebook wall post “the new Audi R8 commercial is socool!” may be tagged as “Consumer Products” (allcategories)->“Automobiles” (categories)->“Audi” (brand)->“R8” (productmodel), “commercial” (ad content), “so cool” (product mention/feedback),and/or the like.

FIG. 6D provides an example logic flow diagram illustrating taxonomytree definition within embodiments of the ATMOS. Within embodiments, thetaxonomy logic rules may be pre-defined by ATMOS panel experts usingstructured data dictionaries. In one implementation, the ATMOS maydetermine the “low level” categories based on structured datadictionaries 650. For example, in one implementation, the TV show modelmay be designed based on a TV program guide to ensure the reportinglabel and naming is followed. In this way, the taxonomy may accelerateand automate the model development process.

The ATMOS may extract sample text 653 for a TV show from social media bya text miner (e.g., the text analytics engine 583 c in FIG. 5C) toidentify most commonly used key words by the social media users. Forexample, user comments posted on the Facebook page of “The Big BangTheory” (e.g., see 186 b in FIG. 1D) may be sampled to extract keywords; if words such as “Werewolf” (characters), “Bontemp” (fictionaltown in the story), etc., are frequently used in the comments, thetaxonomy engine may adopt these words for taxonomy tags, e.g., the keyword text in descriptor (see 643 f in FIG. 6B). This process may enrichthe dictionary and provide inputs to defining taxonomy rules. The ATMOSmay be associated with the identified key words with a level for thetagging 655. For example, in one implementation, the keywords identifiedfrom sampled social media raw texts (e.g., Tweets, Facebook posts, etc.)may be associated with appropriate nodes, e.g., level 0, 1, 2, 5, etc.

In a further implementation, ATMOS may identify keywords set for each TVshow/topic including words spelled differently but meant to refer thesame entity 657 to expand the query scope, e.g. “COCA-COLA,”“COCA-COLA'S,” “COKE,” “COKE'S,” “COCA COLA,” “COCACOLA,” etc. Withinimplementations, a variety of logical combination of such key words maybe coded as a logical rule 660 with AND, OR, Not operator, e.g.

Coke (Level 0) [ coke + good] | [ coke + excellent] (Level 1) [ coke +good] | [ coke + awesome] (Level 1) [ coke in advert + too much taste tocall zero] (Level2) ...

wherein the “+” is an AND operator and “|” is an OR operator. The ATMOSmay establish a taxonomy tree combining the rules and store thegenerated logic taxonomy tree in a taxonomy database (e.g., see 585 a inFIG. 5C).

FIGS. 6E-6F provides an example logic flow illustrating taxonomy logicrule application within embodiments of the ATMOS. In one embodiment,continuing on with 552 in FIG. 5C, e.g., upon receiving unstructureddata for analytics and tagging, etc., ATMOS may parse the unstructureddata for a first level analytics, e.g., whether it is related to a TVshow, and/or a brand product 680., and may generate a first tagassociated with the unstructured data with a brand name 682 by queryingon each brand in the brand database 684. The ATMOS may then retrieve ataxonomy logic tree for the brand “Coke” 686, as shown in the aboveexample.

In one implementation, ATMOS may follow the taxonomy tree for “Coke,”forming a query on a second category, e.g., brand name and positivementions 690. In the above example, the taxonomy tree may apply a queryon “Coke+Good” etc. If such text is found, ATMOS may generate a sub tagassociated with the unstructured data with “Coke+good” 693 a. If not,the ATMOS may apply the taxonomy for an alternative query key terms,e.g., “Coke+Excellent” 693 b, and/or the like. Similarly, if that isfound, ATMOS may generate a sub tag “Coke Excellent” with theunstructured data 695.

Continuing on with FIG. 6F, the taxonomy engine may progressively queryon “Coke” and key word texts, e.g., “too much taste,” 696, etc. If suchkey words are found 697, the ATMOS may generate s sub tag (e.g., level2) with the unstructured data 698 a. If not, the ATMOS may move on withthe next taxonomy rule.

Within implementations, during the taxonomy rule execution process, eachtaxonomy rule may be executed at a leaf node and then the next higherlevel may be computed. Upon finishing with a taxonomy tree, the ATMOSmay generate a matching score of the applied taxonomy logics 699. Forexample, the score may be based on a similarity percentage of the keyterms in each node of the taxonomy tree and the compared unstructureddata segment. To maintain efficiency, taxonomy execution models may belogically partitioned. For example, for a segment of raw text, the sameunstructured text segment may be executed through different models andscores generated may be merged and aggregated. When the similarity scoreis greater than a threshold (e.g., 80%, etc.) 6100, the current tagginggenerated from 682-698 a may be saved 6102. Otherwise, the ATMOS mayproceed with a different taxonomy model, e.g., at 686 in FIG. 6E.

In one implementations, ATMOS may apply one or more taxonomy logics toan unstructured data segment, as the data segment (e.g., a Tweet, aFacebook post, etc.) may be related to one or more brand products, etc.

In further implementations, the taxonomy tagging mechanism may beassociated with a weighting score at each “node” of the taxonomyhierarchy. For example, in the above example for “Coke,” if the ATMOSreceives a client request to analyze consumer impression about a newproduct of Coca Cola on social media analytics, the ATMOS may performtaxonomy mining upon unstructured data from the social media (e.g.,consumer comments). Each “node” may be progressively associated with aweight score to determine consumer impression. For example, when thedata comprises “Coke,” a level 0 weight score may be assigned (e.g.,0.1, etc.); when the data comprises “Coke+good” or “Coke+excellent,” alevel 1 weight score may be assigned (e.g., 0.5, etc.); but when thedata comprises “Coke+horrible” or “Coke” with other negativelyindicative adjectives, a negative level 1 weight score may be assigned(e.g., −0.5, etc.). In one implementation, the taxonomy engine maycalculate an overall score of an unstructured data record whenprogressively querying upon taxonomy key terms, and generate statisticalresults of a group of unstructured data to determine the consumerimpression. For example, the ATMOS may generate statistical report as tohow many consumers are positive, neutral, or negative towards “Coke,”based on their calculated scores, as illustrated in one example in FIGS.6E-6F. For another example, the ATMOS may generate popular brands, TVshows that are the mostly mentioned or positively commented from socialmedia users, e.g., see FIG. 10H. In further implementations, the ATMOSmay determine a social group to analyze their social content. Forexample, within the social group, the ATMOS may determine user'sinfluence over

over other members of the population, e.g., whether a user is an“influencer.” For example, when a user posts comments to the CBS show“The Big Bang Theory” on Facebook, and his Facebook friends have “liked”his comments and subsequently watch the show, the user may be consideredas a Facebook “influencer.” In one implementation, the notion ofinfluencer may be specific to a social media platform; a person who isan influencer on one platform may not be an influencer on another.

In one implementation, the social influencer may be determined by socialmedia indices. For example, the ATMOS may analyze prerequisites forconsideration as a social media influencer, such as whether a user hasan account on a social web platform, whether the user has generatedcontent on that platform within the past 30 days, and/or the like.

In one implementation, the ATMOS may calculate social media index of auser to determine an influencer. For each member that meets theprerequisites, a social media index is calculated using variousmeasures. For example, the ATMOS may calculate a reach measure, e.g.,over the past 30 days, the maximum size of the network, which may bemeasured through “friends”, “followers”, or other similar measures. Foranother example, the ATMOS may calculate a frequency measure, e.g., overthe past 30 days, the total number of posts to the platform, which maybe measured through things like “status updates”, “tweets”, or“comments”, depending on the relevant content generation opportunitiesfor the particular platform. For another example, the ATMOS maycalculate a resonance measure, e.g., over the past 30 days, the totalnumber of responses to the individual's content. Depending on theplatform, these responses may take the form of “retweets”, comments on“posts” or “status updates”, or direct messages responses to theindividual. The responses may or may not need to come from individualswithin the population being measured.

In one implementation, the calculated measures are then ranked againstthe same measure from other individuals in his or her demographic group.The demographic measures may include, but are not limited to, age,gender, race, education, and income. The specific groupings used forage, education, and income can vary based on the population beinganalyzed. An individual is considered an influencer if he or she ranksin the top 20% of at least two of the variables.

In further implementation, the influencer determination may be refinedby product category. Each post made by an individual may be classifiedas mentioning a product, or not mentioning a product, based on textanalysis against a standard taxonomy of products and brands. The volumeof posts in each category can be tabulated, and an individual classifiedas an influencer in any product category which represents at least 20%of his or her product-classified posts. In further implementation, asocial influencer may be specified and/or classified with regard to a TVshow category, a category of products, a category of brands, and/or thelike.

FIG. 7A-7E provide exemplary data diagrams and logic flow diagramsillustrating cross-channel data collection and media measurement withinimplementations of the ATMOS. FIG. 7A provides a block diagramillustrating ATMOS data collection within embodiments of the ATMOS.Within embodiments, the ATMOS may adopt a variety of technologiesincluding flash cookies, mobile applications, browser plug-ins, and/orthe like, to capture media usage across different channels, e.g., TV,mobile, internet, social media, and/or the like. In one implementation,the ATMOS may analyze the media usage data to study the advertisementeffects of a brand product.

In one implementation, ATMOS may track audience activities to content(planning and competitive analysis) and advertising (post analysis, adeffectiveness). In one implementation, the ATMOS may create digitalfootprints on usage as a bi-product of delivering content andadvertising, e.g., via application session ID, cookie, etc., to createdataset that is used for tactical content and advertising decisions. Forexample, the MR-PLATFORM may set up a group of users (e.g., 100,000users, etc.) to track their TV viewing, Mobile usage, Online surfinghistory, advertising exposure, demographic information, productownership info for auto, location, financial services, product usageinformation for CPG/Pharma, and/or the like to provide survey researchfor ad effectiveness. In one implementation, ATMOS may recruit userswith incentive rewards (e.g., the participants may be required to allowATMOS to access their social media content as illustrated in FIGS.5A-5B), e.g., Credit in iTunes or Google App Store account (e.g.,$10/quarter=$40 a year), local coupons/offers based on zip code, etc.

As shown in FIG. 7A, the ATMOS may automatically collect data 705 fromvarious source, e.g., online advertising usage 715, mobile device usage720, TV viewing data 725, social media data (e.g., Facebookposts/conversations, etc.) 730 via API calls (e.g., see FIGS. 5A-5B). Inanother implementation, the ATMOS may employ panelists 710 to configuredata downloads parameters, system maintenance, and/or the like. Forexample, the panelist may comprise social media users that allow ATMOSto access their social profile and content for analytics.

FIGS. 7B-7C provide example flow diagrams illustrating ATMOScross-channel data collection in alternative embodiments of the ATMOS.In one embodiment, a variety of data may be obtained and stored in aATMOS database (e.g., see 219 at FIG. 2A). In one implementation, asshown in FIG. 7B, ATMOS may obtain data from different channel forcross-channel media measurement. For example, the ATMOS may obtain TVchannel changing (e.g., user submission of channel selection) 790 from aATMOS TV client application 795 a (e.g., see FIGS. 7A-7G), mobileadvertising 791 a and mobile application 79 ab usage from a ATMOSapplication 795 b, social media profile 778 a and social media comments778 b from social media (e.g., Facebook Twitter, 750), user exposure totagged advertisement 789 a from a client flash/HTTP cookies 789 b (e.g.,see 375 in FIG. 3D), advertising exposure 790 a (e.g., whether a userclicks on an ad) and website visits 790 b from URL tracking of a userbrowser 797, and/or the like. In one implementation, the ATMOS mayincorporate the variety of data for a cross-channel study of userfeedbacks of an advertisement, a brand, a product, a TV show, and/or thelike.

In a further implementation, the ATMOS may utilize the ATMOS clientcomponent installed at a user mobile device to capture TV viewing in thehome, survey responses, and/or the like. In a further implementation,the ATMOS may adopt a mobile meter to measure mobile usage. In anotherimplementation, the ATMOS may provide a client component which mayprovide history information from a user's personal computer when userconnects his mobile device to his computer to sync up so that the ATMOSmay track online Internet usage of the user, e.g., browsing history,clicks on ads, etc. In a further implementation, the ATMOS may track TVad exposure from a variety of meter data, e.g., TNS, M+, AceMetrix,and/or the like. In further implementations, data may be collectedpassively via mobile phones in almost real time, and/or when phone isbeing charged.

Within implementations, TV distribution may be driven by an over the airbroadcast and a “one-to many” cable infrastructure. Unlike other mediaand industries, like the Internet and the CPG/retail environment, the TVinfrastructure may not create footprints on usage with content (orproduct) distribution. In such cases, the industry may have a panelbased research to understand the size and composition of TV audiences.All decisions related to programming, ad sales, and carriage dealsbetween multiple system operator and cable networks may be based uponpanel data, which may enable research providers to realize outsizedrewards for providing insights.

In one embodiment, the ATMOS may obtain TV viewing data via Return PathData (RPD) from Digital Set Top Boxes (RDTB), which may facilitateanalysis of TV viewing to provide insights on viewing to small networksand small geographies, e.g., local market measurement reflecting a localTV station or cable MSO zone. For another example, metered data from RPDmay be applied to analyze a group of categorized audience (e.g., heavyCoke drinkers) with targeted ad content (e.g., a 30 second commercial ona niche, targeted network).

In further implementations, the ATMOS may study ad effects by collectingdata with regard to user purchasing activities of the advertisedproducts. For example, the ATMOS may track user clicks on a “Buy it Now”button, e.g., see 750 c in FIG. 7G. In another implementation, the ATMOSmay obtain data form retailers, manufactures, Internet players, and/orthe like, wherein real store data, frequent shopper cards usage, and logfiles (Internet shopping) may be analyzed to study placement decisions,increase traffic and sales (ad visits), use the granular traffic(click-stream) data to design customized products/content/advertising toshoppers/viewers, and real time analytics to better manage ad campaigns,and/or the like. In one implementation, a weighing scoring mechanismsimilar to that illustrated in FIG. 2I may be employed, but expanded toa variety of cross channel tracking data, to analyze ad effects within across channel dataset.

In further implementations, the MR-PLATFORM may access to non-livemedia, such as, Charter, TiVo, Rentrak, Internet TV (e.g., Google TV,Apple TV, and/or the like, and incorporate viewing data for analysis(e.g., see FIG. 2H).

In further implementation, Internet measurement data (e.g., from ISPdata, etc.) may be collected, e.g., HTTP cookies, click-stream data withdemographic information, and/or the like.

In further implementations, mobile usage may be tracked via specificapplications (e.g., in a similar manner to log file analysis), anonymouslocation based tracking of cell users, and/or the like.

FIG. 7C shows data collection via a proxy server within implementationsof the ATMOS. For example, in one implementation, a ATMOS clientapplication 795 b operated on a user device may collect and sendinformation such as social application usage, GPS location, response tosurveys, etc., to a database 719. In further implementations, socialapplication usage and mobile advertising data may be passed to a ATMOSproxy server 788 (e.g., see FIG. 7E), and/or a VPN server, which mayforward it to the database 719. In one implementation, TV viewing datamay be provided to the database 719 via a TV remote application 795 a(and/or a TV measurement network). In another implementation, socialmedia engagement data (e.g., user posts, comments on the social media)750 may be sent to the database as well. In further implementations, theATMOS may obtain mobile search behavior of a user, exposure to socialmedia (e.g., user viewing friends' recommendations on social media,etc.), and/or the like.

FIG. 7D provides a data flow diagram illustrating ATMOS data flowbetween entities within alternative embodiments of the ATMOS. Withinembodiments, a ATMOS database 719 may obtain data from Facebook server782 a and Twitter server 782 b via API calls (e.g., see FIGS. 5A-5C). Infurther implementations, the access may be validated and/or authorizedby ATMOS panelists. In further implementations, other media exposure 782e (e.g., see FIG. 7B-9C), survey data 782 f (e.g., from synchronizedquestionnaire, or other questionnaires handled by panelists, etc.),purchasing data 782 c, may be provided to the database. In a furtherimplementation, the cross-channel data may be stored with a cloudstorage 782 d.

In one implementation, a client (e.g., a user, a merchant for analyticsreport, etc.) may access the ATMOS analytics server 755 via a website784, which may in turn operate with a text analytics platform 783 c toanalyze social content, cross-channel data, and/or the like.

FIG. 7E shows a logic flow diagram illustrating monitoring user devicedata transmission across different channels within embodiments of theATMOS. Within embodiments, the ATMOS may instantiate a proxy server tomonitor data in/out of the user mobile device 740, wherein the data maybe transmitted to/from different channels. In one implementation, theproxy server may receive data 742, and extract information from thereceived data 745 to determine a data type and monitor the user deviceactivities which may indicate advertisement delivery/usage information.For example, if the data comprises a website URL 746, the ATMOS maydetermine whether the URL comprises an advertisement component 748. Ifyes, the ATMOS may determine characteristics of the advertisement 749,e.g., classification of the advertisement (e.g., media genre, mediasource, content description, etc.). In further implementations, theATMOS may determine whether the advertisement running on the URL is anautomatic advertisement, or requires user's manual trigger (e.g., userclicks for display). The ATMOS may obtain identifying information, e.g.,an Ad ID, 755, to generate prompt questions, e.g., at 312 in FIG. 3B.

In further implementations, the ATMOS may identify an advertisementcomprised in a URL link via advertisement image recognition. Forexample, the ATMOS may identify graphical contents contained in a URLlink based on empirical pattern of web content format. Such empiricalpatterns may be classified per URL link type (e.g., a shopping site linksuch as Amazon.com may have an advertisement placed at the center frameof the web page; a news link such as New York Times page may have anadvertisement placed at the side/bottom bar of the web page, and/or thelike). For another example, the ATMOS may identify dynamic contents onthe web page, such as, but not limited to flash contents, banners,applets, and/or the like, as displayed advertisements.

Within implementations, upon obtaining an image capture of anadvertisement, the ATMOS may adopt software packages to identifycontents of the advertisement (e.g., a featured product name, a brandname, etc.) so that it can be associated with a user's ad exposure. Inone implementation, the ATMOS may generate a unique identifierindicative of visual characteristics of the captured ad graphicalcontents (e.g., a matrix representation of sampling of the captured adimage, etc.), and form a query on an ad database based on the uniqueidentifier. In another implementation, the ATMOS may adopt softwarepackages similar to, but not limited to Google image search, and/or thelike. Further details of advertisement image match may be similar tothat described in U.S. Pat. No. 7,565,139, entitled “Image Based SearchEngine for Mobile Phone with Camera,” which is herein expresslyincorporated by reference.

In one implementation, the ATMOS may dissect an advertisementidentification from the advertisement embedded in the web content (e.g.,the URL link) to determine a product name, a brand name, and/or thelike, which the user has been exposed to. In further implementations,the captured graphical advertisement contents may be tagged withmetadata in compliance with formats associated with an advertisement,e.g., exif data tags (which may include unique advertising identifiersin the software tags, in the inter-operability tags, in the extensionname/ID, extension description tags, and/or the like). Other graphicalmetadata formats may also be contemplated such as XMP, GIF, IPTCinformation interchange model metadata format, and/or the like.

In further implementations, the ATMOS may determine whether a userclicked a URL link comprising media program content, e.g., a Youtubelink, etc. The ATMOS may extract an identifier, e.g., a web ID, etc., todetermine the name of the media program. In another implementation, theATMOS may obtain excerpts of the media program, and determine a name ofthe media program via an embedded digital signature. In furtherimplementation, the ATMOS may set time stamp on the user's click on theURL link comprising a media program to record how long the user has beenexposed to the media program. Further implementations of the adidentification are discussed in FIG. 7F.

In further implementations, the ATMOS may track a user's browsinghistory by monitoring a stream of “clicks” the user has submitted on hismobile device.

For example, the ATMOS may monitor user's “clicks” to determine a typeof the click, e.g., usage of media playing, visits of a different URLlink, posting of social media contents, usage of an application, and/orthe like. Using an app. The ATMOS may then determine advertisementexposure associated with each “click” (e.g., via ad image identificationillustrated in FIG. 7F).

In another implementation, the received data may comprise applicationinformation from the user device 751. The proxy server may ascertain anapplication inventory list of the user device 753, and/or applicationgroup sharing information 754. For example, one or more users who areFacebook friends, may form a group to share their interested TV watchlist and viewing status via ATMOS, and such information may be capturedby the proxy server.

In another implementation, the received data may comprise indication ofmedia usage 762, e.g., channel selection, atmospherics data, etc., theATMOS may determine whether the TV program on the selected channel hasbeen listened, watched, and/or streamed 766. The ATMOS may determine atitle of the media program 767, and retrieve ad tags embedded in themedia program from a media table to determine user ad exposureinformation, e.g., as discussed in FIGS. 3B-3C. In furtherimplementations, the received data may comprise survey responses/socialmessages sent 763 to the ATMOS, as further discussed in FIGS. 5A-5E.

In one implementation, the ATMOS may generate media analytics report 768based on the obtained media data, including information as to userimpressions to a brand name product, TV shows, etc., e.g., as shown inFIGS. 10A-10H.

In one implementation, the ATMOS may provide advertisers solutions todesign advertising campaigns as to which type of media to place an ad,developing media plans with the optimal mix across media, determine theimpact of advertising on brand awareness, favorability measures, intentto buy and actual purchase across media (ROI). In anotherimplementation, the ATMOS may provide advertising measurement solutionsusing emerging sources of data, e.g., media consumption data (TV,online, mobile, social, etc.), shopper data for key categories, and/orthe like.

In one implementation, the ATMOS may provide clients with insights oncampaign effectiveness and recommendations on optimal media allocationusing survey questionnaires (e.g., as further discussed in FIGS. 3A-3D)based on statistical modeling and regression analysis. In furtherimplementations, the impact of each media may be separately analyzed tomake recommendation on optimal spend and predict sales based upon surveyresponses.

In further implementations, the ATMOS may link different type of datafor cross channel analysis. In one implementation, the ATMOS may createa unified dataset that profiles audiences for TV and online mediaconsumption, wherein each viewing source (e.g., individual TV set,household TV set, etc.) with viewing and ad exposure information for TVand online is associated with a unique identifier. For example, ATMOSmay adopt direct linkage by getting TV and online data for the samehousehold (e.g., via ISP, cable provider, etc.). For another example,the ATMOS may segment TV and online data, and link using segments (e.g.,segmented by program category, zip code, air time, etc.). For anotherexample, the ATMOS may devise and distribute survey questions about TVviewing and linking with the respondents' online surfing data.

In one embodiment, the ATMOS may obtain media consumption data from avariety of channels, such as, but not limited to geographicallydispersed TV viewing data sets (e.g., CANOE), persons tracking with EPGsor embedded in TV software or cell phones, metering data from STB,individual smartphone (e.g., Apple iPhone, etc.) based tracking (e.g.,social content, persons' watching activities, etc.), and/or the like.

FIG. 7F provides a block diagram illustrating example infrastructure ofadvertisement recognition within implementations of the ATMOS. Withinimplementations, the ATMOS may measure which advertisement audience hasbeen exposed to by mining the monitored data in/out of the user mobiledevice, user computer, and/or the like (see e.g., 740 at FIG. 7E). Forexample, URL links and data file may be collected and sent 772 by a datacollection software 771 running at a client component instantiated on auser mobile device. In another implementation, the data collection maybe performed at a proxy or VPN server.

For digital advertising (online and mobile), ATMOS may classify theadvertisements via a hybrid manual/automatic process. For example, ATMOSmay adopt a mobile or PC-based system (using a software meter, VPN,and/or Proxy Server, among other technologies) to pass along to astaging server the URL of the ad, along with the actual file (typicallya .gif, .jpg, or .png). For example, upon receiving ad data includingURLs and associated digital files (e.g., media program excerpt files,etc.) via a network connection 773, the ATMOS server 774 may compare thereceived advertisement data with ad profiles in a database 775. Thefilename, file size, and other data may be compared against an addatabase, and if the ad has already been classified, then the new adexposure event is transmitted to a classification engine 776 classifiedbased on classification rules in the database. Classification rules mayinclude brand and product mentions, as well as ad size and otherdescriptors. If a match is not found, then the ad is put in a queue formanual classification by a ATMOS representative. For example, the ATMOSrepresentative may identify the object (e.g., a product, a brand name,etc.) that is advertised within the ad.

In one implementation, the advertisement may be identified via graphicalcontent match, as discussed in FIG. 7E. In an alternativeimplementation, the ad identification may be performed via an automatedsystem, by which ads that do not match any items in the ad database maybe examined automatically by a computer program for clues as to theproper classification. For example, when an ad that mentions Acura inthe ad image, the ATMOS may automatically classify the ad as an Acuraadvertisement. Within implementations, the ATMOS may perform characterrecognition procedures (e.g., optical character recognition, etc.) toobtain key terms from advertisement images for advertising exposureidentification. Such ad identification may be used for advertisingeffectiveness measurement. Numerous OCR engines may be adopted, such asGOCR, Java OCR, OmniPage, SimpleOCR, and/or the like.

FIGS. 8A-8E provide exemplary mobile screen shots illustrating userinterfaces within embodiments of the ATMOS. As shown in FIG. 8A, theATMOS user interface may comprise a section for social media status,e.g., the Facebook user “John Smith is watching The Big Bang Theory onCBS” 820. The user may also view a list of his friends' status 805,e.g., what the friends are watching.

In one implementation, the user may view a list of channel program 810schedules, and may elect to tap on the screen to choose one of thelisted channels. In a further implementation, the user may select toallow ambient monitoring 850, so that the ATMOS may “listen-in” andsubmit atmospherics data to the ATMOS server, as discussed at 241 inFIG. 2B.

In further implementations, the user may click on the “Prompts” 820button and view a drop down list of survey questions, e.g., as shown inFIG. 8B. The survey question may be generated based on the media contentthe user has been watching, as discussed in FIG. 3B. Upon the usersubmitting a response, as shown in FIG. 8C, the user response may bepopulated as a social media message 830. In one implementation, theuser's friends may view the user's activity and “likes” the user'sresponse 831 a, commented on the response 831 b, and/or follow the linkof the survey and participate in the survey 831 c.

In further implementations, as shown in FIG. 8D, the user may tap on the“Social” 815 button and launch a drop down panel for social media inputs815 a. In one implementation, the ATMOS may generate an automaticmessage template for the user based on the media ad tags, e.g., the userlikes an embedded product placement 815 b. For another example, the usermay manually type texts to update his social media status 815 c.

In further implementations, as shown in FIG. 8E, if the user elects tosubmit a message on social media indicating he likes the embeddedproduct placement 835, the user's friends may be aware of the product.For example, the user's friends may like the post 835 a, comment on theproduct 835 b. For another example, the ATMOS may feed a link directingto a merchant site comprising the placed product along with the socialmedia message, and the user's friend may follow the link to learn moreabout the placed product via the social media 835 c.

FIGS. 8F-8G provide example mobile screens illustrating synchronized adswithin implementations of the ATMOS. In one implementation, ATMOS mayprovide a static (e.g., textual) ad 820 b in a prompts drop-down menu820, as shown in FIG. 8F. For example, when the user selected channel“CBS” has “The Big Bang Theory” on air, and the media content arrives ata timestamp wherein a pair of “white framed sunglasses” is tagged in thescene, ATMOS may generate a pop-up ad 820 b and provide a link for auser to tap on to check out for more details.

FIG. 8G shows an example interactive ad. For example, the ATMOS maygenerate an interactive ad including a screen shot of the TV programcontaining the placed products. The featured products may be tagged(e.g., highlighted by white-line boxes) in the screen image, e.g., apair of “XYZ-designer French style sunglasses” 850 a, and “DDD Red PolkaDots Bikini Halter Top” 850 b. A user may tap on a “Buy it Now” label850 c to check out more details, and/or be directed to a merchant siteto transact a sale.

In a further implementation, the interactive ad may comprise a userrating of the featured product showing beneath the product. In oneimplementation, the user rating may be obtained from historical userrating data, social media rating, and/or the like. In oneimplementation, the user may enter his own rating by tapping on the box850 d.

In a further implementation, the user may browse the interactive ads(e.g., including screen shots from the TV program comprising productplacement tags, etc.) by going to a previous page 851, and/or a nextpage 852. In further implementations, the user may elect to browseinteractive ads associated with the TV program in a variety ways. Forexample, the user may elect to view a list of all ads 855 a; may electto view by season episode 855 b; may elect to view by character 855 c(e.g., products carried by, or associated with the character names inthe show); may elect to view by item category 855 d (e.g., apparel,accessories, furniture, hair products, etc.), and/or the like. Infurther implementations, the user may initiate a search on desiredproducts 858. For example, if the user is interested in a red hat thecharacter “Penny” wore in one of the scenes, the user may form a queryon the embedded ads table based on key terms “Penny,” “red,” “hat,” etc.

FIGS. 8H-8L provide example mobile screen shots illustrating a ATMOS TVremote client component within embodiments of the ATMOS. In oneimplementation, as shown in FIG. 8H, a user may receive TV guide (e.g.,863) as a list of channel program information 860. The user may alsoreceive live TV information updates in a pop-up window 861 at a “live”section 862. The user may further configure parameters in a “Remotes”section 864 and “Settings” 865, as further illustrated in FIGS. 8J-8L.

When the user tap on the “live” section 862, the user may view a list ofmost viewed programs, e.g., ranked by registered ATMOS users. The usermay also see a list of program that the user's social friends arewatching 868, and a list of programs the user has selected 866.

FIG. 8I provides example screens illustrating the “Remotes” 864 at FIG.8H within implementations of the ATMOS. For example, upon plugging aninfrared accessory (e.g., see 120 in FIG. 1B) to a user mobile device,the user may turn the mobile device (e.g., an Apple iPhone, iPod,iTouch, iPad, BlackBerry, Google Android, Palm, etc.) into an infraredTV remote control. Upon tapping on “remote” 864, the user may view avirtual TV remote panel which comprises control buttons for TV programinformation 870 from which the user may tap to select channels, adjustvolume levels, etc. The ATMOS may provide a set-top box control panel tothe user for the user to control live/on-demand video playing, and/orthe like. The user may also configure DVR control 870 a, live TV 870 b,on demand TV 870 c, TiVo 870 d, TV guide 870 d and intelligent TV (e.g.,Apple TV 871, Google TV 872), and/or the like. For another example, theuser may view an array of fast keys for different channels 873.

FIGS. 8J-8K provides example screens illustrating the “Setting” 865 atFIG. 8H within implementations of the ATMOS. In one implementation, theuser may configure TV provider 875 a, set-top box 875 b, Smart TV 875 c,TiVo 875 d, DVD 875 e parameters for the mobile TV remote for ATMOS. Inone implementation, the user may enter the zip code 876 to receive alist of TV providers available in the area, and select his own provider.In another implementation, the user may elect to choose “use TV” only,or to set-up and test a set-top box for his television set. In furtherimplementations, the user may configure TV and DVD parameters so thatthe infrared plug-in accessory may query an address of the TV and DVDset. In one implementation, the user may select a TV brand 880 from abrand list, and then select a type of the TV. For example, the user maytap on a few testing buttons under each type, e.g., “Power,” “Volume,”“Select,” etc., to test whether the remote control works for the TV.

In another implementation, the user may configure DVDs via the ATMOS.The ATMOS may initiate an automatic scan upon user selection, and/orreceive an indication from the user of the DVD brand. Upon indicatingthe DVD brand, the user may test connection with the DVD set to select aDVD type.

In another implementation, the user may enter a zip code to configurethe TV provider 875 a, so that the ATMOS may provide a list of TVproviders for the user to choose.

FIG. 8K provides an exemplary mobile screen shot illustrating socialprofile of a TV program within implementations of the ATMOS. In oneimplementation, the user may select a TV program (e.g., “Channel 5” 885)on a channel to view its social profile. In one implementation, the usermay tap on “Check-in” 886 to populate a Facebook message with regard tothe checked TV program, and/or “Twitter” icon 887 to share suchinformation on Twitter. For example, to “Check-in,” the user may selectpost onto his Facebook wall by typing a message 886 a, and/or share theautomatically generated message 886 a by ATMOS. In anotherimplementation, if the user chooses to “Tweet,” the user may view a listof “discussion” 888 Tweets with regard to the show “The Big BangTheory,” and may generate his own Tweet.

In one implementation, the user may view a social rating 889 of the TVprogram “The Big Bang Theory” under its profile. For example, the rating889 may be given by social users of ATMOS who has viewed the program,and the user may elects to submit his own rating 889 a. In a furtherimplementation, the user may obtain a list of “social watching” 890 tosee a list of social users (e.g., social media users who has allowedATMOS to access their social profiles, etc.) who is watching theselected TV program.

FIG. 9A provides an exemplary mobile screen shot (e.g., 511 a in FIGS.5A-5B) illustrating user authorization of ATMOS access to the user'sFacebook content within embodiments of the ATMOS. In one implementation,a user may receive a message (e.g., a pop-up message window, an email,etc.) requesting a user to elect whether to allow ATMOS to access hisFacebook content. For example, the user may tap on a link in themessage, e.g., “Yes” or “Not Now” to grant or deny permission to hisFacebook content.

In another implementation, as shown in FIG. 9A, a user may configuresocial network connection settings under the settings (e.g., see 865 atFIG. 8H) of ATMOS client mobile application. In one implementation, theATMOS may send an access request so that a user may view a prompt fromhis mobile device 911, and may elect allow or disallow the ATMOS accessattempt 902. The user may be directed to a Facebook login page 910,wherein Facebook may request user to provide login credentials to verifythe permission authorization. For example, the user may enter his emailaddress 913 and password 915 to login to Facebook. For another example,if the user does not have a Facebook account yet, upon tapping on “Yes,”the user may be directed to create a new account. In anotherimplementation, if the user's mobile application (e.g., an iPhoneFacebook app, a mobile browser, etc.) stores user previously enteredlogin credentials, the user mobile device may send the login credentialsto the social media to proceed with access authorization, so that theuser may not need to view the login page 910 to manually provide usercredentials.

Upon providing Facebook login credentials and verification of Facebook,the user may receive a request for permission screen 911 to select to“Allow” 908 ATMOS to access the user's Facebook profile. In furtherimplementations, the user may configure access parameters in a similarmanner as illustrated in FIGS. 9B-8C.

FIGS. 9B-8C provide exemplary web-based user interface (e.g., 511 b inFIGS. 5A-5B) illustrating user authorization of ATMOS access to theuser's social media content within embodiments of the ATMOS. Forexample, in one implementation, a user may access a web-based ATMOSconfiguration page via an Internet browser (e.g., Internet Explorer,Safari, Firefox, etc.) to bridge his social media accounts with ATMOS.In one implementation, the user may select a list of social mediaplatforms 920 to join ATMOS, e.g., the user may click on checkboxes tochoose Facebook 921 a, Twitter 921 b, Google+ 921 c, Tumblr 921 d and/orthe like. In further implementations, the user may specify other socialmedia platforms not listed by ATMOS by typing a URL address, e.g.,“FourSquare,” etc. Upon selecting the social media, the user may click“Send Request” 925 so that ATMOS may send a connection request to theselected social media platforms, e.g., Facebook 921 a and Twitter 921 bas shown in the example of FIG. 9B. In another implementation, the usermay click “Cancel” 926 to abort the access control configuration.

In one implementation, upon submitting the request 925, the social mediaplatform may request login confirmation 925. For example, the user maybe redirected to the social media homepage to login. For anotherexample, the user may be presented a pop-up window 928 for the socialanalytics to connect with Facebook, e.g., the user may need to provideemail 926 and password 927 to login to Facebook.

As shown in FIG. 9C, upon providing user Facebook credentials, the usermay configure access scope 927 for Facebook content. For example, theuser may elect to allow ATMOS to access his user profile, e.g., the usermay select among a list of checkboxes for user name, user address, useremail, user phone number, work information, education information, dateof birth, pages, groups, networks, and/or the like. For another example,the user may configure the ATMOS may access his friends information 929,e.g., the user may allow the ATMOS to obtain a number 0 friends 929 abut may not allow the ATMOS to access details of the friends list. Foranother example, the user may allow the ATMOS to access his wall posts930, e.g., the user may allow the ATMOS to access his post on his ownwall 930 a (including sharing links, posted photos, status update,messages, etc.). The user may conditionally allow ATMOS to access hispost on his friend's wall when the friend allows access to his wall 930c. Similarly, the user may conditionally allow ATMOS to access theuser's likes/dislikes when the liked or disliked item owner allows ATMOSaccess 930 d as well.

FIGS. 9D-9F provide exemplary web-based user interface illustratingmedia analytics within embodiments of the ATMOS. For example, as shownin FIG. 9D, a ATMOS user/client (e.g., an advertising merchant, a TVmedia producer, etc.) who may desire to know audience feedbacks to anad, TV program, and/or the like, may access a media analytics reports940 module. In one implementation, the user may select tabs fordifferent options to view analytics of a targeted object (e.g., a TVshow, an advertisement, etc.). For example, a use may elect to viewcomments categorized by people 941, TV shows 942, brand names 943,products 944, media sources 944, and/or the like.

In one implementation, as shown in FIG. 9D, if the user selects TV shows942, the user may provide information of the TV show by selecting a TVnetwork, (e.g., “CBS” at 945 a), genre (e.g., “Comedy” at 945 b), showname (e.g., “The Big Bang Theory” at 945 c), from a drop down list. Inanother implementation, the user may manually enter a name of the TVshow 945 d to query on a TV show database at ATMOS.

In one implementation, the user may configure analytics parameters suchas social content source 948 (e.g., check on “Facebook” and “Twitter”),time range 949 (e.g., between a specified time and present). The usermay further select a presentation format, e.g., whether to view rawtextual comments 950, or a visualized summary 955 (e.g., plain format955 a, chart/plots 955 b, or table 955 c). For example, if raw text 950is selected, the user may view a list of Facebook comments 950 a, andTweets 950 b related to the show “The Big Bang Theory.”

In another implementation, as shown in FIG. 9E, if the user elects toview analytics report in a visualized format 955, the summary may bepresented in a plain textual format, e.g., 960 a, showing statisticalresults of the positive, neutral and/or negative comments. For anotherexample, charts/plots 960 b, and a table 960 c summary may be presented.In one implementation, the analytics summary may be broken down todifferent categories, e.g., the comments for each category “CBS,” “TheBig Bang Theory,” “Characters,” “Music” of the show, and/or the like.

FIGS. 10A-10H provides exemplary user interfaces of media analyticsreports within embodiments of the ATMOS. FIG. 10 A provides an exemplaryATMOS media analytics UI flow. Within implementations, upon obtainingmeasurement data from a variety of data channels (e.g., social media,URL links, mobile metering, etc.), the ATMOS may provide web based mediaanalytics platform for a user to access via a Internet web browser. Therecorded data analytics as shown in FIGS. 10A-10F may be explored via avariety of data file formats including XML, ASC, and/or the like,through the database export mechanism. Within implementations, theseexported files may be imported to analytical tools, such as SAS, etc.,wherein various statistical analysis may be engaged. In oneimplementation, selections made from the dashboard, e.g., a genreselection, 1041 of FIG. 10H, may be used as the query filter prior tothe export of the physical information, and as such, and data importedinto the package will be limited to the selector (e.g., genre, name,etc.). The output from a standard statistical output from package SASmay be used in its entirety and/or parsed for the dashboard report. Forexample, the ATMOS may download the statistical output in a text formatand present it in a dashboard user interface. In another example, theSAS output may be reported in a statistical format (e.g., commadelineated XML file), which may enhance parsing for the dashboard reportgeneration.

For example, the ATMOS may provide a welcome/login screen to a user1005, wherein the user may be a ATMOS client, such as a TV producer, anadvertiser, a merchant, and/or the like, who may be interested to learntheir TV audience statistics, brand/products impressions, and/or thelike.

Upon user login at 1005, the ATMOS may provide a customized dashboardpage 1008, e.g., as further illustrated in one implementation at FIGS.10B/10H. The dashboard page may provide an overview of the mediaanalytics results, such as generated report types 1010, availablestatistics charts/plots, and/or the like. For example, the ATMOS mayperform analytics to generate advertising reports 1012 (e.g.,advertisement delivery reports 1012 a, etc.), audience reports 1015(e.g., an audience summary report 1015 a, audience reports by genderreach 1015 b, by age reach 1015 c, reports per day of week 1015 d,reports per hour during a day part 1015 e, etc.).

FIG. 10B provides an exemplary screen shot for media analytics dashboard1008. In one implementation, a user may elect to configure mediaanalytics parameters. For example, the user may select an object of themedia analytics, which may be a media platform, a TV channel, anapplication platform, and/or the like. The dashboard board may provide adropdown menu to allow the user to select various applications and mediaplatforms e.g., the user may select view application usage of“Foursquare” 1018. In other implementations, the user may selecttargeted ATMOS social users for the study, e.g., by checking user gender1020, age groups 1021, user interface types (e.g., ATMOS application,mobile, web, etc.), phone types 1023, day part time range 1024, userlocation 1025, and/or the like. In further implementations, the userconfigured social user group may be saved as a group for furtheranalysis.

In one implementation, the media analytics dashboard page may provide aplot of total ATMOS user sessions 1028 based on the user configuredATMOS social user group. For example, the personal trending datastatistics 1028 may further comprise plots of number of users, number ofsessions, number of new users, median/mean session length, geographicregions of users, and/or the like. In another implementation, ATMOS mayprovide analysis of ATMOS application usage statistics 1029, such as atotal number of sessions, a total number of registered users, a mediansession length per user, and/or the like. In another implementation, theATMOS may provide a summary table 1030 showing the study of userselected reporting application “Foursquare,” including audience activereach, total visits, visits per person, total web page views, page viewper person, duration, time per person, web user gender, age, and/or thelike. For example, a report on the number of users may summarize thenumber of users an application (e.g., “Foursquare”) has had over periodof tracking to indicate whether the number is growing or declining. Thereport may also analyze user sessions in different scenarios (e.g.,whether by a single user). For example, when a mobile site has beenvisited for 50 times, but 30 of those times it was from the same user,versus another month the number visits from different users was 25, thenumber of users may not be considered as increased.

For another example, a report on number of sessions may include numberof sessions by all users within the last calendar month, which may betracked on a monthly trending basis to indicate how often anapplication/site was seen by all users. For another example, a report onnumber of new users may show the number of new users for the lastcalendar month using the application or mobile site. For anotherexample, the median/mean session length may indicate the time spent onapplication/site. For another example, the geo graphic region of usersmay show a geographical distribution of users engaging or visiting theapplication/advertisement

FIG. 10C provides an exemplary audience summary report 1015 a. As shownin FIG. 10C, the user may select to view “summary” 1030 a, for a datatable showing: for each reporting application (e.g., Facebook, ESPNSportsCenter, etc.), statistics data for Unique Audience, Percent ActiveReach, Time per person (minutes), TPP/Month, Number of visits, Number ofpages viewed, Visits/person, Male/Female (for unique and non-uniqueusers/visitors), Age groups (for unique and non-unique users/visitors).For example, the unique audience may be calculated as the total realnumber of users of an app/site without double counting users that havereused the site/app more than once in a month, and/or the like. Thepercent active reach of all smartphone users may be calculated as thepercentage of users that checked a particular mobile website orapplication within the last calendar month, e.g., when there are 100smartphone users, 88 checked Facebook within the last calendar month,active users equal 88%. The time per person (minutes) may be calculatedas the amount of time spent during a day on a site/app by avg. activeuser, e.g., if there was 88 active users and they used a site/app for atotal of 20 hours during the day, then the time per person is equal to22.72 minutes (20/88=0.2272). The TPP/month may be calculated as thetime per person per month that is spent on the site. e.g., if there are70 users and in total and they spend woo hours on the site/app then thetime per person per month would be 11.11 hours (1000/90). The number ofvisits may be calculated as the total number of times an app/site wasvisited during the last calendar month by all users. The number of pagesviewed may be calculated as the total number pages viewed on an app/siteduring the last calendar month by all users. The visits/person may becalculated as the number of visits to app/site on a per active usersbasis. In one implementation, the above mentioned metrics may beassessed by male/female gender classification (for unique and non-uniqueusers/visitors), age groups (for unique and non-unique users/visitors),e.g., age brackets may be 18-24, 25-34, 35-44, 45-54, 55+, etc.

FIG. 10D provides an exemplary audience report per gender reach 1015 b.As shown in FIG. 10C, the user may select to view “Gender Reach” 1030 b,for a data table showing application report classified by gender,including unique audience active reach (%), total duration (in minutes),time per person, time per person for the month, total web page views,pages per person, total visits, visits per person, and/or the like. Forexample, the audience report by gender may include data for eachapp/site broken up by gender. For example, Facebook may provide male andfemale break of user base, about time men spend on the app, number ofpages, etc.

FIG. 10F provides an exemplary audience report per age reach 1015 c. Asshown in FIG. 10C, the user may select to view “Age Reach” 1030 c, for adata table showing application report classified by age, including totalaudience, unique audience, active reach (%), total duration (inminutes), time per person, time per person over a month period, totalpage views, pages per person, total visits, visits per person, and/orthe like broken up in different age groups.

FIG. 10G provides an exemplary audience report per day 1015 e. As shownin FIG. 10G, the user may select to view “Day Part” 1030 e, for a datatable showing application report classified by hours of a day. Forexample, for this report, for this report, data for each app/site may bebroken up by the hour of the day. For example, as shown in FIG. 10G, theYoutube app usage data may be broken up by the hour of the day.

FIG. 10 H provides an exemplary analytics report within an alternativeimplementation of the ATMOS. In one implementation, the ATMOS maycollect social data from social media platforms of different socialgroups (e.g., 1066 a, 1066 b), e.g., the social groups determined byuser configured parameters as shown in FIG. 10B, to determine a varietyof measures of social TV watching status, such as, but not limited toprofile measures 1051, liking measures 1052, brand measures 1053, TVshows watching measures 1054, and/or the like. In one implementation,the ATMOS may define a social group based on age ranges, gender,demographic information, geographical location, income level,educational level, and/or the like. In another implementation, the usermay configure parameters for TV shows, brands/products, social groups,and/or the like, e.g., genre 1041, actors 1042, networks 1043, first runtime 1044, airing time 1045, 6 brand 1046, product type 1047, date 1048,geography 1049, influencer s 1050, age 1020, 7 gender 1021, and/or thelike.

For example, in one implementation, for a specified TV show (e.g., “BigBang Theory” at “CBS”), the profile measure 1051 may provide social datametrics with regard to social groups, such as percentage of influencersin a social group (e.g., social users whose watching recommendationshave been followed by other social users, etc.), percentage of users whoengage in watching the show, percentage of users who is distracted fromwatching the show (e.g., via atmospherics analysis as illustrated inFIG. 2E), number of friends who have engaged in the TV watching, numberof posts per week, a number of brand pages the social group hasfollowed, number of photos the social group has posted, and/or the like.In further implementations, the ATMOS may analyze the social mediacontent to provide liking measures 1052, such as the most positivelycommented 10 brand pages, music pages, TV show pages, and/or the like;brand measures 1053, such as the top 10 brands, top 5 products mentionedwithin a social group, and/or the like; the top TV shows watched 1054,and/or the like.

ATMOS Controller

FIG. 11 shows a block diagram illustrating embodiments of a ATMOScontroller. In this embodiment, the ATMOS controller 1101 may serve toaggregate, process, store, search, serve, identify, instruct, generate,match, and/or facilitate interactions with a computer through socialnetwork and electronic commerce technologies, and/or other related data.

Typically, users, which may be people and/or other systems, may engageinformation technology systems (e.g., computers) to facilitateinformation processing. In turn, computers employ processors to processinformation; such processors 1103 may be referred to as centralprocessing units (CPU). One form of processor is referred to as amicroprocessor. CPUs use communicative circuits to carry and passencoded (e.g., binary) signals acting as instructions to bring aboutvarious operations. These instructions may be operational and/or datainstructions containing and/or referencing other instructions and datain various processor accessible and operable areas of memory 1129 (e.g.,registers, cache memory, random access memory, etc.). Such communicativeinstructions may be stored and/or transmitted in batches (e.g., batchesof instructions) as programs and/or data components to facilitatedesired operations. These stored instruction codes, e.g., programs, mayengage the CPU circuit components and other motherboard and/or systemcomponents to perform desired operations. One type of program is acomputer operating system, which, may be executed by CPU on a computer;the operating system enables and facilitates users to access and operatecomputer information technology and resources. Some resources that maybe employed in information technology systems include: input and outputmechanisms through which data may pass into and out of a computer;memory storage into which data may be saved; and processors by whichinformation may be processed. These information technology systems maybe used to collect data for later retrieval, analysis, and manipulation,which may be facilitated through a database program. These informationtechnology systems provide interfaces that allow users to access andoperate various system components.

In one embodiment, the ATMOS controller 1101 may be connected to and/orcommunicate with entities such as, but not limited to: one or more usersfrom user input devices 1111; peripheral devices 1112; an optionalcryptographic processor device 1128; and/or a communications network1113.

Networks are commonly thought to comprise the interconnection andinteroperation of clients, servers, and intermediary nodes in a graphtopology. It should be noted that the term “server” as used throughoutthis application refers generally to a computer, other device, program,or combination thereof that processes and responds to the requests ofremote users across a communications network. Servers serve theirinformation to requesting “clients.” The term “client” as used hereinrefers generally to a computer, program, other device, user and/orcombination thereof that is capable of processing and making requestsand obtaining and processing any responses from servers across acommunications network. A computer, other device, program, orcombination thereof that facilitates, processes information andrequests, and/or furthers the passage of information from a source userto a destination user is commonly referred to as a “node.” Networks aregenerally thought to facilitate the transfer of information from sourcepoints to destinations. A node specifically tasked with furthering thepassage of information from a source to a destination is commonly calleda “router.” There are many forms of networks such as Local Area Networks(LANs), Pico networks, Wide Area Networks (WANs), Wireless Networks(WLANs), etc. For example, the Internet is generally accepted as beingan interconnection of a multitude of networks whereby remote clients andservers may access and interoperate with one another.

The ATMOS controller 1101 may be based on computer systems that maycomprise, but are not limited to, components such as: a computersystemization 1102 connected to memory 1129.

Computer Systemization

A computer systemization 1102 may comprise a clock 1130, centralprocessing unit (“CPU(s)” and/or “processor(s)” (these terms are usedinterchangeable throughout the disclosure unless noted to the contrary))1103, a memory 1129 (e.g., a read only memory (ROM) 1106, a randomaccess memory (RAM) 1105, etc.), and/or an interface bus 1107, and mostfrequently, although not necessarily, are all interconnected and/orcommunicating through a system bus 1104 on one or more (mother)board(s)1102 having conductive and/or otherwise transportive circuit pathwaysthrough which instructions (e.g., binary encoded signals) may travel toeffectuate communications, operations, storage, etc. The computersystemization may be connected to a power source 1186; e.g., optionallythe power source may be internal. Optionally, a cryptographic processor1126 and/or transceivers (e.g., ICs) 1174 may be connected to the systembus. In another embodiment, the cryptographic processor and/ortransceivers may be connected as either internal and/or externalperipheral devices 1112 via the interface bus I/O. In turn, thetransceivers may be connected to antenna(s) 1175, thereby effectuatingwireless transmission and reception of various communication and/orsensor protocols; for example the antenna(s) may connect to: a TexasInstruments WiLink WL1283 transceiver chip (e.g., providing 802.11n,Bluetooth 5.0, FM, global positioning system (GPS) (thereby allowingATMOS controller to determine its location)); Broadcom BCM4329FKUBGtransceiver chip (e.g., providing 802.11n, Bluetooth 2.1+EDR, FM, etc.);a Broadcom BCM4750IUB8 receiver chip (e.g., GPS); an InfineonTechnologies X-Gold 618-PMB9800 (e.g., providing 2G/3G HSDPA/HSUPAcommunications); and/or the like. The system clock typically has acrystal oscillator and generates a base signal through the computersystemization's circuit pathways. The clock is typically coupled to thesystem bus and various clock multipliers that may increase or decreasethe base operating frequency for other components interconnected in thecomputer systemization. The clock and various components in a computersystemization drive signals embodying information throughout the system.Such transmission and reception of instructions embodying informationthroughout a computer systemization may be commonly referred to ascommunications. These communicative instructions may further betransmitted, received, and the cause of return and/or replycommunications beyond the instant computer systemization to:communications networks, input devices, other computer systemizations,peripheral devices, and/or the like. It should be understood that inalternative embodiments, any of the above components may be connecteddirectly to one another, connected to the CPU, and/or organized innumerous variations employed as exemplified by various computer systems.

The CPU comprises at least one high-speed data processor adequate toexecute program components for executing user and/or system-generatedrequests. Often, the processors themselves may incorporate variousspecialized processing units, such as, but not limited to: integratedsystem (bus) controllers, memory management control units, floatingpoint units, and even specialized processing sub-units like graphicsprocessing units, digital signal processing units, and/or the like.Additionally, processors may include internal fast access addressablememory, and be capable of mapping and addressing memory 1129 beyond theprocessor itself; internal memory may include, but is not limited to:fast registers, various levels of cache memory (e.g., level 1, 2, 5,etc.), RAM, etc. The processor may access this memory through the use ofa memory address space that is accessible via instruction address, whichthe processor can construct and decode allowing it to access a circuitpath to a specific memory address space having a memory state. The CPUmay be a microprocessor such as: AMD's Athlon, Duron and/or Opteron;ARM's application, embedded and secure processors; IBM and/or Motorola'sDragonBall and PowerPC; IBM's and Sony's Cell processor; Intel'sCeleron, Core (2) Duo, Itanium, Pentium, Xeon, and/or XScale; and/or thelike processor(s). The CPU interacts with memory through instructionpassing through conductive and/or transportive conduits (e.g., (printed)electronic and/or optic circuits) to execute stored instructions (i.e.,program code) according to conventional data processing techniques. Suchinstruction passing facilitates communication within the ATMOScontroller and beyond through various interfaces. Should processingrequirements dictate a greater amount speed and/or capacity, distributedprocessors (e.g., Distributed ATMOS), mainframe, multi-core, parallel,and/or super-computer architectures may similarly be employed.Alternatively, should deployment requirements dictate greaterportability, smaller Personal Digital Assistants (PDAs) may be employed.

Depending on the particular implementation, features of the ATMOS may beachieved by implementing a microcontroller such as CAST's R8051XC2microcontroller; Intel's MCS 51 (i.e., 8051 microcontroller); and/or thelike. Also, to implement certain features of the ATMOS, some featureimplementations may rely on embedded components, such as:Application-Specific Integrated Circuit (“ASIC”), Digital SignalProcessing (“DSP”), Field Programmable Gate Array (“FPGA”), and/or thelike embedded technology. For example, any of the ATMOS componentcollection (distributed or otherwise) and/or features may be implementedvia the microprocessor and/or via embedded components; e.g., via ASIC,coprocessor, DSP, FPGA, and/or the like. Alternately, someimplementations of the ATMOS may be implemented with embedded componentsthat are configured and used to achieve a variety of features or signalprocessing.

Depending on the particular implementation, the embedded components mayinclude software solutions, hardware solutions, and/or some combinationof both hardware/software solutions. For example, ATMOS featuresdiscussed herein may be achieved through implementing FPGAs, which are asemiconductor devices containing programmable logic components called“logic blocks”, and programmable interconnects, such as the highperformance FPGA Virtex series and/or the low cost Spartan seriesmanufactured by Xilinx. Logic blocks and interconnects can be programmedby the customer or designer, after the FPGA is manufactured, toimplement any of the ATMOS features. A hierarchy of programmableinterconnects allow logic blocks to be interconnected as needed by theATMOS system designer/administrator, somewhat like a one-chipprogrammable breadboard. An FPGA's logic blocks can be programmed toperform the operation of basic logic gates such as AND, and XOR, or morecomplex combinational operators such as decoders or mathematicaloperations. In most FPGAs, the logic blocks also include memoryelements, which may be circuit flip-flops or more complete blocks ofmemory. In some circumstances, the ATMOS may be developed on regularFPGAs and then migrated into a fixed version that more resembles ASICimplementations. Alternate or coordinating implementations may migrateATMOS controller features to a final ASIC instead of or in addition toFPGAs. Depending on the implementation all of the aforementionedembedded components and microprocessors may be considered the “CPU”and/or “processor” for the ATMOS.

Power Source

The power source 1186 may be of any standard form for powering smallelectronic circuit board devices such as the following power cells:alkaline, lithium hydride, lithium ion, lithium polymer, nickel cadmium,solar cells, and/or the like. Other types of AC or DC power sources maybe used as well. In the case of solar cells, in one embodiment, the caseprovides an aperture through which the solar cell may capture photonicenergy. The power cell 1186 is connected to at least one of theinterconnected subsequent components of the ATMOS thereby providing anelectric current to all subsequent components. In one example, the powersource 1186 is connected to the system bus component 1104. In analternative embodiment, an outside power source 1186 is provided througha connection across the I/O 1108 interface. For example, a USB and/orIEEE 1394 connection carries both data and power across the connectionand is therefore a suitable source of power.

Interface Adapters

Interface bus(ses) 1107 may accept, connect, and/or communicate to anumber of interface adapters, conventionally although not necessarily inthe form of adapter cards, such as but not limited to: input outputinterfaces (I/O) 1108, storage interfaces 1109, network interfaces 1110,and/or the like. Optionally, cryptographic processor interfaces 1127similarly may be connected to the interface bus. The interface busprovides for the communications of interface adapters with one anotheras well as with other components of the computer systemization.Interface adapters are adapted for a compatible interface bus. Interfaceadapters conventionally connect to the interface bus via a slotarchitecture. Conventional slot architectures may be employed, such as,but not limited to: Accelerated Graphics Port (AGP), Card Bus,(Extended) Industry Standard Architecture ((E)ISA), Micro ChannelArchitecture (MCA), NuBus, Peripheral Component Interconnect (Extended)(PCI(X)), PCI Express, Personal Computer Memory Card InternationalAssociation (PCMCIA), and/or the like.

Storage interfaces 1109 may accept, communicate, and/or connect to anumber of storage devices such as, but not limited to: storage devices1114, removable disc devices, and/or the like. Storage interfaces mayemploy connection protocols such as, but not limited to: (Ultra)(Serial) Advanced Technology Attachment (Packet Interface) ((Ultra)(Serial) ATA(PI)), (Enhanced) Integrated Drive Electronics ((E)IDE),Institute of Electrical and Electronics Engineers (IEEE) 1394, fiberchannel, Small Computer Systems Interface (SCSI), Universal Serial Bus(USB), and/or the like.

Network interfaces 1110 may accept, communicate, and/or connect to acommunications network 1113. Through a communications network 1113, theATMOS controller is accessible through remote clients 1133 b (e.g.,computers with web browsers) by users 1133 a. Network interfaces mayemploy connection protocols such as, but not limited to: direct connect,Ethernet (thick, thin, twisted pair 10/100/1000 Base T, and/or thelike), Token Ring, wireless connection such as IEEE 802.11a-x, and/orthe like. Should processing requirements dictate a greater amount speedand/or capacity, distributed network controllers (e.g., DistributedATMOS), architectures may similarly be employed to pool, load balance,and/or otherwise increase the communicative bandwidth required by theATMOS controller. A communications network may be any one and/or thecombination of the following: a direct interconnection; the Internet; aLocal Area Network (LAN); a Metropolitan Area Network (MAN); anOperating Missions as Nodes on the Internet (OMNI); a secured customconnection; a Wide Area Network (WAN); a wireless network (e.g.,employing protocols such as, but not limited to a Wireless ApplicationProtocol (WAP), I-mode, and/or the like); and/or the like. A networkinterface may be regarded as a specialized form of an input outputinterface. Further, multiple network interfaces 1110 may be used toengage with various communications network types 1113. For example,multiple network interfaces may be employed to allow for thecommunication over broadcast, multicast, and/or unicast networks.

Input Output interfaces (I/O) 1108 may accept, communicate, and/orconnect to user input devices 1111, peripheral devices 1112,cryptographic processor devices 1128, and/or the like. I/O may employconnection protocols such as, but not limited to: audio: analog,digital, monaural, RCA, stereo, and/or the like; data: Apple Desktop Bus(ADB), IEEE 1394a-b, serial, universal serial bus (USB); infrared;joystick; keyboard; midi; optical; PC AT; PS/2; parallel; radio; videointerface: Apple Desktop Connector (ADC), BNC, coaxial, component,composite, digital, Digital Visual Interface (DVI), high-definitionmultimedia interface (HDMI), RCA, RF antennae, S-Video, VGA, and/or thelike; wireless transceivers: 802.11a/b/g/n/x; Bluetooth; cellular (e.g.,code division multiple access (CDMA), high speed packet access(HSPA(+)), high-speed downlink packet access (HSDPA), global system formobile communications (GSM), long term evolution (LTE), WiMax, etc.);and/or the like. One typical output device may include a video display,which typically comprises a Cathode Ray Tube (CRT) or Liquid CrystalDisplay (LCD) based monitor with an interface (e.g., DVI circuitry andcable) that accepts signals from a video interface, may be used. Thevideo interface composites information generated by a computersystemization and generates video signals based on the compositedinformation in a video memory frame. Another output device is a TV set,which accepts signals from a video interface. Typically, the videointerface provides the composited video information through a videoconnection interface that accepts a video display interface (e.g., anRCA composite video connector accepting an RCA composite video cable; aDVI connector accepting a DVI display cable, etc.).

User input devices 1111 often are a type of peripheral device 512 (seebelow) and may include: card readers, dongles, finger print readers,gloves, graphics tablets, joysticks, keyboards, microphones, mouse(mice), remote controls, retina readers, touch screens (e.g.,capacitive, resistive, etc.), trackballs, trackpads, sensors (e.g.,accelerometers, ambient light, GPS, gyroscopes, proximity, etc.),styluses, and/or the like.

Peripheral devices 1112 may be connected and/or communicate to I/Oand/or other facilities of the like such as network interfaces, storageinterfaces, directly to the interface bus, system bus, the CPU, and/orthe like. Peripheral devices may be external, internal and/or part ofthe ATMOS controller. Peripheral devices may include: antenna, audiodevices (e.g., line-in, line-out, microphone input, speakers, etc.),cameras (e.g., still, video, webcam, etc.), dongles (e.g., for copyprotection, ensuring secure transactions with a digital signature,and/or the like), external processors (for added capabilities; e.g.,crypto devices 528), force-feedback devices (e.g., vibrating motors),network interfaces, printers, scanners, storage devices, transceivers(e.g., cellular, GPS, etc.), video devices (e.g., goggles, monitors,etc.), video sources, visors, and/or the like. Peripheral devices ofteninclude types of input devices (e.g., cameras).

It should be noted that although user input devices and peripheraldevices may be employed, the ATMOS controller may be embodied as anembedded, dedicated, and/or monitor-less (i.e., headless) device,wherein access would be provided over a network interface connection.

Cryptographic units such as, but not limited to, microcontrollers,processors 1126, interfaces 1127, and/or devices 1128 may be attached,and/or communicate with the ATMOS controller. A MC68HC16microcontroller, manufactured by Motorola Inc., may be used for and/orwithin cryptographic units. The MC68HC16 microcontroller utilizes a16-bit multiply-and-accumulate instruction in the 16 MHz configurationand requires less than one second to perform a 512-bit RSA private keyoperation. Cryptographic units support the authentication ofcommunications from interacting agents, as well as allowing foranonymous transactions. Cryptographic units may also be configured aspart of the CPU. Equivalent microcontrollers and/or processors may alsobe used. Other commercially available specialized cryptographicprocessors include: Broadcom's CryptoNetX and other Security Processors;nCipher's nShield; SafeNet's Luna PCI (e.g., 7100) series; SemaphoreCommunications' 40 MHz Roadrunner 184; Sun's Cryptographic Accelerators(e.g., Accelerator 6000 PCIe Board, Accelerator 500 Daughtercard); ViaNano Processor (e.g., L2100, L2200, U2400) line, which is capable ofperforming 500+ MB/s of cryptographic instructions; VLSI Technology's 53MHz 6868; and/or the like.

Memory

Generally, any mechanization and/or embodiment allowing a processor toaffect the storage and/or retrieval of information is regarded as memory1129. However, memory is a fungible technology and resource, thus, anynumber of memory embodiments may be employed in lieu of or in concertwith one another. It is to be understood that the ATMOS controllerand/or a computer systemization may employ various forms of memory 1129.For example, a computer systemization may be configured wherein theoperation of on-chip CPU memory (e.g., registers), RAM, ROM, and anyother storage devices are provided by a paper punch tape or paper punchcard mechanism; however, such an embodiment would result in an extremelyslow rate of operation. In a typical configuration, memory 1129 mayinclude ROM 1106, RAM 1105, 13 and a storage device 1114. A storagedevice 1114 may be any conventional computer system storage. Storagedevices may include a drum; a (fixed and/or removable) magnetic diskdrive; a magneto-optical drive; an optical drive (i.e., Blueray, CDROM/RAM/Recordable (R)/ReWritable (RW), DVD R/RW, HD DVD R/RW etc.); anarray of devices (e.g., Redundant Array of Independent Disks (RAID));solid state memory devices (USB memory, solid state drives (SSD), etc.);other processor-readable storage mediums; and/or other devices of thelike. Thus, a computer systemization generally requires and makes use ofmemory.

Component Collection

The memory 1129 may contain a collection of program and/or databasecomponents and/or data such as, but not limited to: operating systemcomponent(s) 1115 (operating system); information server component(s)1116 (information server); user interface component(s) 1117 (userinterface); Web browser component(s) 1118 (Web browser); database(s)1119; mail server component(s) 1121; mail client component(s) 1122;cryptographic server component(s) 1120 (cryptographic server); the ATMOScomponent(s) 1135; and/or the like (i.e., collectively a componentcollection). These components may be stored and accessed from thestorage devices and/or from storage devices accessible through aninterface bus. Although non-conventional program components such asthose in the component collection, typically, are stored in a localstorage device 1114, they may also be loaded and/or stored in memorysuch as: peripheral devices, RAM, remote storage facilities through acommunications network, ROM, various forms of memory, and/or the like.

Operating System

The operating system component 1115 is an executable program componentfacilitating the operation of the ATMOS controller. Typically, theoperating system facilitates access of I/O, network interfaces,peripheral devices, storage devices, and/or the like. The operatingsystem may be a highly fault tolerant, scalable, and secure system suchas: Apple Macintosh OS X (Server); AT&T Plan 7; Be OS; Unix andUnix-like system distributions (such as AT&T's UNIX; Berkley SoftwareDistribution (BSD) variations such as FreeBSD, NetBSD, OpenBSD, and/orthe like; Linux distributions such as Red Hat, Ubuntu, and/or the like);and/or the like operating systems. However, more limited and/or lesssecure operating systems also may be employed such as Apple MacintoshOS, IBM OS/2, Microsoft DOS, Microsoft Windows2000/2003/3.1/95/98/CE/Millenium/NT/Vista/XP (Server), Palm OS, and/orthe like. An operating system may communicate to and/or with othercomponents in a component collection, including itself, and/or the like.Most frequently, the operating system communicates with other programcomponents, user interfaces, and/or the like. For example, the operatingsystem may contain, communicate, generate, obtain, and/or provideprogram component, system, user, and/or data communications, requests,and/or responses. The operating system, once executed by the CPU, mayfacilitate the interaction with communications networks, data, I/O,peripheral devices, program components, memory, user input devices,and/or the like. The operating system may provide communicationsprotocols that allow the ATMOS controller to communicate with otherentities through a communications network 1113. Various communicationprotocols may be used by the ATMOS controller as a subcarrier transportmechanism for interaction, such as, but not limited to: multicast,TCP/IP, UDP, unicast, and/or the like.

Information Server

An information server component 1116 is a stored program component thatis executed by a CPU. The information server may be a conventionalInternet information server such as, but not limited to Apache SoftwareFoundation's Apache, Microsoft's Internet Information Server, and/or thelike. The information server may allow for the execution of programcomponents through facilities such as Active Server Page (ASP), ActiveX,(ANSI) (Objective-) C (++), C# and/or .NET, Common Gateway Interface(CGI) scripts, dynamic (D) hypertext markup language (HTML), FLASH,Java, JavaScript, Practical Extraction Report Language (PERL), HypertextPre-Processor (PHP), pipes, Python, wireless application protocol (WAP),WebObjects, and/or the like. The information server may support securecommunications protocols such as, but not limited to, File TransferProtocol (FTP); HyperText Transfer Protocol (HTTP); Secure HypertextTransfer Protocol (HTTPS), Secure Socket Layer (SSL), messagingprotocols (e.g., America Online (AOL) Instant Messenger (AIM),Application Exchange (APEX), ICQ, Internet Relay Chat (IRC), MicrosoftNetwork (MSN) Messenger Service, Presence and Instant Messaging Protocol(PRIM), Internet Engineering Task Force's (IETF's) Session InitiationProtocol (SIP), SIP for Instant Messaging and Presence LeveragingExtensions (SIMPLE), open XML-based Extensible Messaging and PresenceProtocol (XMPP) (i.e., Jabber or Open Mobile Alliance's (OMA's) InstantMessaging and Presence Service (IMPS)), Yahoo! Instant MessengerService, and/or the like. The information server provides results in theform of Web pages to Web browsers, and allows for the manipulatedgeneration of the Web pages through interaction with other programcomponents. After a Domain Name System (DNS) resolution portion of anHTTP request is resolved to a particular information server, theinformation server resolves requests for information at specifiedlocations on the ATMOS controller based on the remainder of the HTTPrequest. For example, a request such ashttp://123.124.125.126/myInformation.html might have the IP portion ofthe request “123.124.125.126” resolved by a DNS server to an informationserver at that IP address; that information server might in turn furtherparse the http request for the “/myInformation.html” portion of therequest and resolve it to a location in memory containing theinformation “myInformation.html.” Additionally, other informationserving protocols may be employed across various ports, e.g., FTPcommunications across port 21, and/or the like. An information servermay communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the information server communicates with the ATMOS database1119, operating systems, other program components, user interfaces, Webbrowsers, and/or the like.

Access to the ATMOS database may be achieved through a number ofdatabase bridge mechanisms such as through scripting languages asenumerated below (e.g., CGI) and through inter-application communicationchannels as enumerated below (e.g., CORBA, WebObjects, etc.). Any datarequests through a Web browser are parsed through the bridge mechanisminto appropriate grammars as required by the ATMOS. In one embodiment,the information server would provide a Web form accessible by a Webbrowser. Entries made into supplied fields in the Web form are tagged ashaving been entered into the particular fields, and parsed as such. Theentered terms are then passed along with the field tags, which act toinstruct the parser to generate queries directed to appropriate tablesand/or fields. In one embodiment, the parser may generate queries instandard SQL by instantiating a search string with the properjoin/select commands based on the tagged text entries, wherein theresulting command is provided over the bridge mechanism to the ATMOS asa query. Upon generating query results from the query, the results arepassed over the bridge mechanism, and may be parsed for formatting andgeneration of a new results Web page by the bridge mechanism. Such a newresults Web page is then provided to the information server, which maysupply it to the requesting Web browser.

Also, an information server may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses.

User Interface

Computer interfaces in some respects are similar to automobile operationinterfaces. Automobile operation interface elements such as steeringwheels, gearshifts, and speedometers facilitate the access, operation,and display of automobile resources, and status. Computer interactioninterface elements such as check boxes, cursors, menus, scrollers, andwindows (collectively and commonly referred to as widgets) similarlyfacilitate the access, capabilities, operation, and display of data andcomputer hardware and operating system resources, and status. Operationinterfaces are commonly called user interfaces. Graphical userinterfaces (GUIs) such as the Apple Macintosh Operating System's Aqua,IBM's OS/2, Microsoft's Windows2000/2003/3.1/95/98/CE/Millenium/NT/XP/Vista/7 (i.e., Aero), Unix'sX-Windows (e.g., which may include additional Unix graphic interfacelibraries and layers such as K Desktop Environment (KDE), mythTV and GNUNetwork Object Model Environment (GNOME)), web interface libraries(e.g., ActiveX, AJAX, (D)HTML, FLASH, Java, JavaScript, etc. interfacelibraries such as, but not limited to, Dojo, jQuery(UI), MooTools,Prototype, script.aculo.us, SWFObject, Yahoo! User Interface, any ofwhich may be used and) provide a baseline and means of accessing anddisplaying information graphically to users.

A user interface component 1117 is a stored program component that isexecuted by a CPU. The user interface may be a conventional graphic userinterface as provided by, with, and/or atop operating systems and/oroperating environments such as already discussed. The user interface mayallow for the display, execution, interaction, manipulation, and/oroperation of program components and/or system facilities through textualand/or graphical facilities. The user interface provides a facilitythrough which users may affect, interact, and/or operate a computersystem. A user interface may communicate to and/or with other componentsin a component collection, including itself, and/or facilities of thelike. Most frequently, the user interface communicates with operatingsystems, other program components, and/or the like. The user interfacemay contain, communicate, generate, obtain, and/or provide programcomponent, system, user, and/or data communications, requests, and/orresponses.

Web Browser

A Web browser component 1118 is a stored program component that isexecuted by a CPU. The Web browser may be a conventional hypertextviewing application such as Microsoft Internet Explorer or NetscapeNavigator. Secure Web browsing may be supplied with 128 bit (or greater)encryption by way of HTTPS, SSL, and/or the like. Web browsers allowingfor the execution of program components through facilities such asActiveX, AJAX, (D)HTML, FLASH, Java, JavaScript, web browser plug-inAPIs (e.g., FireFox, Safari Plug-in, and/or the like APIs), and/or thelike. Web browsers and like information access tools may be integratedinto PDAs, cellular telephones, and/or other mobile devices. A Webbrowser may communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the Web browser communicates with information servers,operating systems, integrated program components (e.g., plug-ins),and/or the like; e.g., it may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses. Also, in place of a Webbrowser and information server, a combined application may be developedto perform similar operations of both. The combined application wouldsimilarly affect the obtaining and the provision of information tousers, user agents, and/or the like from the ATMOS enabled nodes. Thecombined application may be nugatory on systems employing standard Webbrowsers.

Mail Server

A mail server component 1121 is a stored program component that isexecuted by a CPU 1103. The mail server may be a conventional Internetmail server such as, but not limited to sendmail, Microsoft Exchange,and/or the like. The mail server may allow for the execution of programcomponents through facilities such as ASP, ActiveX, (ANSI) (Objective-)C (++), C# and/or .NET, CGI scripts, Java, JavaScript, PERL, PHP, pipes,Python, WebObjects, and/or the like. The mail server may supportcommunications protocols such as, but not limited to: Internet messageaccess protocol (IMAP), Messaging Application Programming Interface(MAPI)/Microsoft Exchange, post office protocol (POPS), simple mailtransfer protocol (SMTP), and/or the like. The mail server can route,forward, and process incoming and outgoing mail messages that have beensent, relayed and/or otherwise traversing through and/or to the ATMOS.

Access to the ATMOS mail may be achieved through a number of APIsoffered by the individual Web server components and/or the operatingsystem.

Also, a mail server may contain, communicate, generate, obtain, and/orprovide program component, system, user, and/or data communications,requests, information, and/or responses.

Mail Client

A mail client component 1122 is a stored program component that isexecuted by a CPU 1103. The mail client may be a conventional mailviewing application such as Apple Mail, Microsoft Entourage, MicrosoftOutlook, Microsoft Outlook Express, Mozilla, Thunderbird, and/or thelike. Mail clients may support a number of transfer protocols, such as:IMAP, Microsoft Exchange, POP3, SMTP, and/or the like. A mail client maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, themail client communicates with mail servers, operating systems, othermail clients, and/or the like; e.g., it may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, information, and/or responses. Generally,the mail client provides a facility to compose and transmit electronicmail messages.

Cryptographic Server

A cryptographic server component 1120 is a stored program component thatis executed by a CPU 1103, cryptographic processor 1126, cryptographicprocessor interface 1127, cryptographic processor device 1128, and/orthe like. Cryptographic processor interfaces may allow for expedition ofencryption and/or decryption requests by the cryptographic component;however, the cryptographic component, alternatively, may run on aconventional CPU. The cryptographic component allows for the encryptionand/or decryption of provided data. The cryptographic component allowsfor both symmetric and asymmetric (e.g., Pretty Good Protection (PGP))encryption and/or decryption. The cryptographic component may employcryptographic techniques such as, but not limited to: digitalcertificates (e.g., X.509 authentication framework), digital signatures,dual signatures, enveloping, password access protection, public keymanagement, and/or the like. The cryptographic component may facilitatenumerous (encryption and/or decryption) security protocols such as, butnot limited to: checksum, Data Encryption Standard (DES), EllipticalCurve Encryption (ECC), International Data Encryption Algorithm (IDEA),Message Digest 5 (MD5, which is a one way hash operation), passwords,Rivest Cipher (RC5), Rijndael, RSA (which is an Internet encryption andauthentication system that uses an algorithm developed in 1977 by RonRivest, Adi Shamir, and Leonard Adleman), Secure Hash Algorithm (SHA),Secure Socket Layer (SSL), Secure Hypertext Transfer Protocol (HTTPS),and/or the like. Employing such encryption security protocols, the ATMOSmay encrypt all incoming and/or outgoing communications and may serve asnode within a virtual private network (VPN) with a wider communicationsnetwork. The cryptographic component facilitates the process of“security authorization” whereby access to a resource is inhibited by asecurity protocol wherein the cryptographic component effects authorizedaccess to the secured resource. In addition, the cryptographic componentmay provide unique identifiers of content, e.g., employing and MD5 hashto obtain a unique signature for an digital audio file. A cryptographiccomponent may communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Thecryptographic component supports encryption schemes allowing for thesecure transmission of information across a communications network toenable the ATMOS component to engage in secure transactions if sodesired. The cryptographic component facilitates the secure accessing ofresources on the ATMOS and facilitates the access of secured resourceson remote systems; i.e., it may act as a client and/or server of securedresources. Most frequently, the cryptographic component communicateswith information servers, operating systems, other program components,and/or the like. The cryptographic component may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, and/or responses.

The ATMOS Database

The ATMOS database component 1119 may be embodied in a database and itsstored data. The database is a stored program component, which isexecuted by the CPU; the stored program component portion configuringthe CPU to process the stored data. The database may be a conventional,fault tolerant, relational, scalable, secure database such as Oracle orSybase. Relational databases are an extension of a flat file. Relationaldatabases consist of a series of related tables. The tables areinterconnected via a key field. Use of the key field allows thecombination of the tables by indexing against the key field; i.e., thekey fields act as dimensional pivot points for combining informationfrom various tables. Relationships generally identify links maintainedbetween tables by matching primary keys. Primary keys represent fieldsthat uniquely identify the rows of a table in a relational database.More precisely, they uniquely identify rows of a table on the “one” sideof a one-to-many relationship.

Alternatively, the ATMOS database may be implemented using variousstandard data-structures, such as an array, hash, (linked) list, struct,structured text file (e.g., XML), table, and/or the like. Suchdata-structures may be stored in memory and/or in (structured) files. Inanother alternative, an object-oriented database may be used, such asFrontier, ObjectStore, Poet, Zope, and/or the like. Object databases caninclude a number of object collections that are grouped and/or linkedtogether by common attributes; they may be related to other objectcollections by some common attributes. Object-oriented databases performsimilarly to relational databases with the exception that objects arenot just pieces of data but may have other types of capabilitiesencapsulated within a given object. If the ATMOS database is implementedas a data-structure, the use of the ATMOS database 1119 may beintegrated into another component such as the ATMOS component 1135.Also, the database may be implemented as a mix of data structures,objects, and relational structures. Databases may be consolidated and/ordistributed in countless variations through standard data processingtechniques. Portions of databases, e.g., tables, may be exported and/orimported and thus decentralized and/or integrated.

In one embodiment, the database component 1119 includes several tables1119 a-1. A user accounts table 1119 a includes fields such as, but notlimited to: a UserID, UserName, UserPassword, UserAddress, UserDeviceID,UserViewingHistory, UserRating, UserPreference, and/or the like. TheUser table may support and/or track multiple entity accounts on a ATMOS.A Real Time TV table 1119 b includes fields such as, but not limited to:TVChannelID, TVChannelName, TVChannelLogo, TVChannelAirTime,TVChannelProgram, TVChannelAd and/or the like. A Media Program table1119 c includes fields such as, MediaID, MediaName, MediaLength,MediaSignature, MediaBrand, MediaTimeTag, MediaAd, MediaAdSponsor,MediaAirTime, MediaChannelID, and/or the like. A Survey Question table1119 d includes fields such as QuestionID, QuestionCategory,QuestionAdID, QuestionDescription, QuestionResponse, QuestionMediaID,QuestionMediaTimeTag, QuestionUserID, and/or the like. An Ad table 1119e includes fields such as, but not limited to: AdID, AdMerchant,AdFormat, AdProduct, AdText, AdTimeTag, AdMediaID, AdChannelID,AdAudioSignature, and/or the like. An Atmospherics table 1119 f includesfields such as, but not limited to: AtmosID, AtmosType, AtmosTimestamp,AtmosUserID, AtmosDeviceID, AtmosPhotoID, AtmosGPS, AtmosMediaID,AtmosChannelID, and/or the like. A User Device table 1119 g includesfields such as, but not limited to: DeviceID, DeviceType,DeviceHardwareID, DeviceMAC, DeviceAppInventory, and/or the like. AReports table 1119 h includes fields such as, but not limited to:ReportID, ReportTimePeriod, ReportMediaID, ReportChannelID, ReportAdID,ReportType, ReportUserRating, ReportAdEffect, and/or the like. A socialcontent table 1119 i includes fields such as, but not limited to:SocialID, SocialName, SocialUserID, SocialTokenID, SocialUserID,SocialSource, SocialContent, SocialTimeStamp, and/or the like. ATaxonomy table 1119 j includes fields such as, but not limited to:TaxpID, TaxoName, TaxoProducType, TaxoKeyWords, TaxoTreeNode, TaxoLevel,TaxoLabels, and/or the like. A User Token table 1119 k includes fieldssuch as, but not limited to: TokenID, TokenUserID, TokenSocialID,TokenSocialSource, TokenNumber, TokenFile, and/or the like. An AnalyticsWeight Scores table 11191 includes fields such as, but not limited to:ScoreID, ScoreName, ScoreFactorAttribute, ScoreWeight, ScoreDescription,ScoreIndication, and/or the like.

In one embodiment, the ATMOS database may interact with other databasesystems. For example, employing a distributed database system, queriesand data access by search ATMOS component may treat the combination ofthe ATMOS database, an integrated data security layer database as asingle database entity.

In one embodiment, user programs may contain various user interfaceprimitives, which may serve to update the ATMOS. Also, various accountsmay require custom database tables depending upon the environments andthe types of clients the ATMOS may need to serve. It should be notedthat any unique fields may be designated as a key field throughout. Inan alternative embodiment, these tables have been decentralized intotheir own databases and their respective database controllers (i.e.,individual database controllers for each of the above tables). Employingstandard data processing techniques, one may further distribute thedatabases over several computer systemizations and/or storage devices.Similarly, configurations of the decentralized database controllers maybe varied by consolidating and/or distributing the various databasecomponents 1119 a-1. The ATMOS may be configured to keep track ofvarious settings, inputs, and parameters via database controllers.

The ATMOS database may communicate to and/or with other components in acomponent collection, including itself, and/or facilities of the like.Most frequently, the ATMOS database communicates with the ATMOScomponent, other program components, and/or the like. The database maycontain, retain, and provide information regarding other nodes and data.

The ATMOSs

The ATMOS component 1135 is a stored program component that is executedby a CPU. In one embodiment, the ATMOS component incorporates any and/orall combinations of the aspects of the ATMOS that was discussed in theprevious figures. As such, the ATMOS affects accessing, obtaining andthe provision of information, services, transactions, and/or the likeacross various communications networks.

The ATMOS transforms TV program schedule listing information and userchannel selection via ATMOS components, such as real time TV 1042, adsurvey synchronization 1043, atmospherics analysis 1044, audiencestatistics analysis 1045, social media connection 1046, media analytics1047 and/or the like into TV audience viewing data and ad effects data.

The ATMOS component facilitates access of information between nodes maybe developed by employing standard development tools and languages suchas, but not limited to: Apache components, Assembly, ActiveX, binaryexecutables, (ANSI) (Objective-) C (++), C# and/or .NET, databaseadapters, CGI scripts, Java, JavaScript, mapping tools, procedural andobject oriented development tools, PERL, PHP, Python, shell scripts, SQLcommands, web application server extensions, web developmentenvironments and libraries (e.g., Microsoft's ActiveX; Adobe AIR, FLEX &FLASH; AJAX; (D)HTML; Dojo, Java; JavaScript; jQuery(UI); MooTools;Prototype; script.aculo.us; Simple Object Access Protocol (SOAP);SWFObject; Yahoo! User Interface; and/or the like), WebObjects, and/orthe like. In one embodiment, the ATMOS server employs a cryptographicserver to encrypt and decrypt communications. The ATMOS component maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, theATMOS component communicates with the ATMOS database, operating systems,other program components, and/or the like. The ATMOS may contain,communicate, generate, obtain, and/or provide program component, system,user, and/or data communications, requests, and/or responses.

Distributed ATMOSs

The structure and/or operation of any of the ATMOS node controllercomponents may be combined, consolidated, and/or distributed in anynumber of ways to facilitate development and/or deployment. Similarly,the component collection may be combined in any number of ways tofacilitate deployment and/or development. To accomplish this, one mayintegrate the components into a common code base or in a facility thatcan dynamically load the components on demand in an integrated fashion.

The component collection may be consolidated and/or distributed incountless variations through standard data processing and/or developmenttechniques. Multiple instances of any one of the program components inthe program component collection may be instantiated on a single node,and/or across numerous nodes to improve performance throughload-balancing and/or data-processing techniques. Furthermore, singleinstances may also be distributed across multiple controllers and/orstorage devices; e.g., databases. All program component instances andcontrollers working in concert may do so through standard dataprocessing communication techniques.

The configuration of the ATMOS controller may depend on the context ofsystem deployment. Factors such as, but not limited to, the budget,capacity, location, and/or use of the underlying hardware resources mayaffect deployment requirements and configuration. Regardless of if theconfiguration results in more consolidated and/or integrated programcomponents, results in a more distributed series of program components,and/or results in some combination between a consolidated anddistributed configuration, data may be communicated, obtained, and/orprovided. Instances of components consolidated into a common code basefrom the program component collection may communicate, obtain, and/orprovide data. This may be accomplished through intra-application dataprocessing communication techniques such as, but not limited to: datareferencing (e.g., pointers), internal messaging, object instancevariable communication, shared memory space, variable passing, and/orthe like.

If component collection components are discrete, separate, and/orexternal to one another, then communicating, obtaining, and/or providingdata with and/or to other component components may be accomplishedthrough inter-application data processing communication techniques suchas, but not limited to: Application Program Interfaces (API) informationpassage; (distributed) Component Object Model ((D)COM), (Distributed)Object Linking and Embedding ((D)OLE), and/or the like), Common ObjectRequest Broker Architecture (CORBA), Jini local and remote applicationprogram interfaces, JavaScript Object Notation (JSON), Remote MethodInvocation (RMI), SOAP, process pipes, shared files, and/or the like.Messages sent between discrete component components forinter-application communication or within memory spaces of a singularcomponent for intra-application communication may be facilitated throughthe creation and parsing of a grammar. A grammar may be developed byusing development tools such as lex, yacc, XML, and/or the like, whichallow for grammar generation and parsing capabilities, which in turn mayform the basis of communication messages within and between components.

For example, a grammar may be arranged to recognize the tokens of anHTTP post command, e.g.:

-   -   w3c-post http:// . . . Value1

where Value1 is discerned as being a parameter because “http://” is partof the grammar syntax, and what follows is considered part of the postvalue. Similarly, with such a grammar, a variable “Value1” may beinserted into an “http://” post command and then sent. The grammarsyntax itself may be presented as structured data that is interpretedand/or otherwise used to generate the parsing mechanism (e.g., a syntaxdescription text file as processed by lex, yacc, etc.). Also, once theparsing mechanism is generated and/or instantiated, it itself mayprocess and/or parse structured data such as, but not limited to:character (e.g., tab) delineated text, HTML, structured text streams,XML, and/or the like structured data. In another embodiment,inter-application data processing protocols themselves may haveintegrated and/or readily available parsers (e.g., JSON, SOAP, and/orlike parsers) that may be employed to parse (e.g., communications) data.Further, the parsing grammar may be used beyond message parsing, but mayalso be used to parse: databases, data collections, data stores,structured data, and/or the like. Again, the desired configuration maydepend upon the context, environment, and requirements of systemdeployment.

For example, in some implementations, the ATMOS controller may beexecuting a PHP script implementing a Secure Sockets Layer (“SSL”)socket server via the information server, which listens to incomingcommunications on a server port to which a client may send data, e.g.,data encoded in JSON format. Upon identifying an incoming communication,the PHP script may read the incoming message from the client device,parse the received JSON-encoded text data to extract information fromthe JSON-encoded text data into PHP script variables, and store the data(e.g., client identifying information, etc.) and/or extractedinformation in a relational database accessible using the StructuredQuery Language (“SQL”). An exemplary listing, written substantially inthe form of PHP/SQL commands, to accept JSON-encoded input data from aclient device via a SSL connection, parse the data to extract variables,and store the data to a database, is provided below:

<?PHP header(′Content-Type: text/plain′); // set ip address and port tolisten to for incoming data $address = ‘192.168.0.100’; $port = 255; //create a server-side SSL socket, listen for/accept incomingcommunication $sock = socket_create(AF_INET, SOCK_STREAM, 0);socket_bind($sock, $address, $port) or die(‘Could not bind to address’);socket_listen($sock); $client = socket_accept($sock); // read input datafrom client device in 1024 byte blocks until end of message do { $input= “”; $input = socket_read($client, 1024); $data .= $input; }while($input != “”); // parse data to extract variables $obj =json_decode($data, true); // store input data in a databasemysql_connect(″201.408.185.132″,$DBserver,$password); // access databaseserver mysql_select(″CLIENT_DB.SQL″); // select database to appendmysql_query(“INSERT INTO UserTable (transmission) VALUES ($data)”); //add data to UserTable table in a CLIENT databasemysql_close(″CLIENT_DB.SQL″); // close connection to database ?>

Also, the following resources may be used to provide example embodimentsregarding SOAP parser implementation:

http://www.xav.com/perl/site/lib/SOAP/Parser.htmlhttp://publib.boulder.ibm.com/infocenter/tivihelp/v2r1/index.jsp?topic=/com.ibm.IBMDI.doc/referenceguide295.htm

and other parser implementations:

http://publib.boulder.ibm.com/infocenter/tivihelp/v2r1/index.jsp?topic=/com.ibm.IBMDI.doc/referenceguide259.htm

all of which are hereby expressly incorporated by reference.

Additional embodiments of the ATMOS may comprise the following:

1. A TV mobile control processor-implemented method, comprising:

obtaining TV program schedule listing data;

providing the obtained TV program schedule listing data to a generalpurpose user mobile device communicatively coupled to an infraredcommunication component;

receiving a user media program selection message from the generalpurpose user mobile device,

-   -   wherein the general purpose user mobile device transmits a TV        remote channel selection indication to a TV set via the infrared        communication component,    -   wherein the user media program selection message and the TV        remote channel selection indication comprise the same user        selected channel;

determining whether the received user media program selection messageindicates a user watching event;

determining a user watching time length associated with the userselected channel when the received user media program selection messageis determined to indicate the user watching event; and

generating and storing a user watching event log file including the userselected channel and the determined user watching time length.

2. The method of embodiment 1, further comprising:

filtering the received user media program selection message when thereceived user media program selection message is determined not toindicate a user watching event.

3. The method of embodiment 1, wherein the general purpose user mobiledevice comprises any of a smartphone, a personal data assistant, acellular phone, a laptop, and a tablet computer

4. The method of embodiment 1, wherein the TV program schedule listingdata is transmitted via any of a cellar network, a 3G network, and aWifi network.

5. The method of embodiment 1, wherein the general purpose user mobiledevice transmits a TV remote channel selection indication to a TVset-top box via the infrared plug-in component.

6. The method of embodiment 1, wherein the TV remote channel selectionmessage comprises a non-live media program selection message.

7. The method of embodiment 1, wherein the non-live media programselection message comprises any of a DVD control message, a DVR controlmessage and an on-demand media control message.

8. The method of embodiment 1, wherein the determining whether thereceived user media program selection message indicates a user watchingevent comprises:

calculating a time lapse between two consecutively received programselection messages; and

determining whether the time lapse is sufficiently long to indicate auser watching event.

9. The method of embodiment 1, wherein the determining a user watchingtime length comprises determining whether a time lapse between twoconsecutively received program selection messages exceeds a cappingthreshold.

10. The method of embodiment 1, wherein the determining a user watchingtime length comprises applying watching time caps based on any of TVon/off events, set-top box on/off events and heuristics.

11. The method of embodiment 1, further comprising feeding the userwatching event log file for user viewing data record associated with theselected media program.

12. The method of embodiment 1, wherein the general purpose user mobiledevice is configured to automatically scan on a communication stack fora physical address of a TV set.

13. The method of embodiment 1, wherein the general purpose user mobiledevice receives user submitted TV parameters to scan for a TV set.

14. The method of embodiment 1, further comprising:

obtaining an atmospherics data artifact from the atmospherics datapackage.

15. The method of embodiment 1, further comprising:

extracting user instant activities information based on analysis of theatmospherics data artifact.

16. The method of embodiment 15, further comprising:

generating a user viewing status indication based on the user instantactivities information; and

incorporating the user viewing status indication into viewer measurementdata of the user selected channel

17. The method of embodiment 1, wherein the received TV program schedulelisting data comprises a plurality of ad tags.

18. The method of embodiment 17, further comprising:

-   -   retrieving an ad tag associated with the user selected media        program from the TV program schedule listing data.

19. The method of embodiment 18, further comprising:

extracting key terms from the ad tags based by parsing ad contents;

querying a survey question list based on the extracted key terms;

-   -   generating and sending a survey question from the query to the        user mobile device; and    -   obtaining a user reaction to the survey question.

20. The method of embodiment 17, further comprising:

generating a social watching status message; and

populating the social watching status message to a social mediaplatform.

21. A TV mobile control system, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

-   -   obtain TV program schedule listing data;    -   provide the obtained TV program schedule listing data to a        general purpose user mobile device communicatively coupled to an        infrared communication component;    -   receive a user media program selection message from the general        purpose user mobile device,        -   wherein the general purpose user mobile device transmits a            TV remote channel selection indication to a TV set via the            infrared communication component,        -   wherein the user media program selection message and the TV            remote channel selection indication comprise the same user            selected channel;    -   determine whether the received user media program selection        message indicates a user watching event;    -   determine a user watching time length associated with the user        selected channel when the received user media program selection        message is determined to indicate the user watching event; and    -   generate and storing a user watching event log file including        the user selected channel and the determined user watching time        length.

22. The system of embodiment 21, wherein the processor further issuesinstructions to:

filter the received user media program selection message when thereceived user media program selection message is determined not toindicate a user watching event.

23. The system of embodiment 21, wherein the general purpose user mobiledevice comprises any of a smartphone, a personal data assistant, acellular phone, a laptop, and a tablet computer

24. The system of embodiment 21, wherein the TV program schedule listingdata is transmitted via any of a cellar network, a 3G network, and aWifi network.

25. The system of embodiment 21, wherein the general purpose user mobiledevice transmits a TV remote channel selection indication to a TVset-top box via the infrared plug-in component.

26. The system of embodiment 21, wherein the TV remote channel selectionmessage comprises a non-live media program selection message.

27. The system of embodiment 21, wherein the non-live media programselection message comprises any of a DVD control message, a DVR controlmessage and an on-demand media control message.

28. The system of embodiment 21, wherein the determining whether thereceived user media program selection message indicates a user watchingevent comprises:

-   -   calculating a time lapse between two consecutively received        program selection messages; and    -   determining whether the time lapse is sufficiently long to        indicate a user watching event.

29. The system of embodiment 21, wherein the determining a user watchingtime length comprises determining whether a time lapse between twoconsecutively received program selection messages exceeds a cappingthreshold.

30. The system of embodiment 21, wherein the determining a user watchingtime length comprises applying watching time caps based on any of TVon/off events, set-top box on/off events and heuristics.

31. The system of embodiment 21, wherein the processor issuesinstructions to feed the user watching event log file for user viewingdata record associated with the selected media program.

32. The system of embodiment 21, wherein the general purpose user mobiledevice is configured to automatically scan on a communication stack fora physical address of a TV set.

33. The system of embodiment 21, wherein the general purpose user mobiledevice receives user submitted TV parameters to scan for a TV set.

34. The system of embodiment 21, wherein the processor further issuesinstructions to:

-   -   obtain an atmospherics data artifact from the atmospherics data        package.

35. The system of embodiment 21, wherein the processor further issuesinstructions to:

extract user instant activities information based on analysis of theatmospherics data artifact.

36. The system of embodiment 25, wherein the processor further issuesinstructions to:

generate a user viewing status indication based on the user instantactivities information; and

incorporate the user viewing status indication into viewer measurementdata of the user selected channel

37. The system of embodiment 21, wherein the received TV programschedule listing data comprises a plurality of ad tags.

38. The system of embodiment 17, wherein the processor further issuesinstructions to:

-   -   retrieve an ad tag associated with the user selected media        program from the TV program schedule listing data.

39. The system of embodiment 38, wherein the processor further issuesinstructions to:

extract key terms from the ad tags based by parsing ad contents;

query a survey question list based on the extracted key terms;

-   -   generate and sending a survey question from the query to the        user mobile device; and    -   obtain a user reaction to the survey question.

40. The system of embodiment 37, wherein the processor further issuesinstructions to:

generate a social watching status message; and

populate the social watching status message to a social media platform.

41. A TV mobile control processor-readable storage medium storingprocessor-executable instructions to:

-   -   obtain TV program schedule listing data;    -   provide the obtained TV program schedule listing data to a        general purpose user mobile device communicatively coupled to an        infrared communication component;    -   receive a user media program selection message from the general        purpose user mobile device,        -   wherein the general purpose user mobile device transmits a            TV remote channel selection indication to a TV set via the            infrared communication component,        -   wherein the user media program selection message and the TV            remote channel selection indication comprise the same user            selected channel;    -   determine whether the received user media program selection        message indicates a user watching event;    -   determine a user watching time length associated with the user        selected channel when the received user media program selection        message is determined to indicate the user watching event; and    -   generate and storing a user watching event log file including        the user selected channel and the determined user watching time        length.

42. The medium of embodiment 41, wherein the processor further issuesinstructions to:

filter the received user media program selection message when thereceived user media program selection message is determined not toindicate a user watching event.

43. The medium of embodiment 41, wherein the general purpose user mobiledevice comprises any of a smartphone, a personal data assistant, acellular phone, a laptop, and a tablet computer

44. The medium of embodiment 41, wherein the TV program schedule listingdata is transmitted via any of a cellar network, a 3G network, and aWifi network.

45. The medium of embodiment 41, wherein the general purpose user mobiledevice transmits a TV remote channel selection indication to a TVset-top box via the infrared plug-in component.

46. The medium of embodiment 41, wherein the TV remote channel selectionmessage comprises a non-live media program selection message.

47. The medium of embodiment 41, wherein the non-live media programselection message comprises any of a DVD control message, a DVR controlmessage and an on-demand media control message.

48. The medium of embodiment 41, wherein the determining whether thereceived user media program selection message indicates a user watchingevent comprises:

-   -   calculating a time lapse between two consecutively received        program selection messages; and    -   determining whether the time lapse is sufficiently long to        indicate a user watching event.

49. The medium of embodiment 41, wherein the determining a user watchingtime length comprises determining whether a time lapse between twoconsecutively received program selection messages exceeds a cappingthreshold.

50. The medium of embodiment 41, wherein the determining a user watchingtime length comprises applying watching time caps based on any of TVon/off events, set-top box on/off events and heuristics.

51. The medium of embodiment 41, further storing instructions to feedthe user watching event log file for user viewing data record associatedwith the selected media program.

52. The medium of embodiment 41, wherein the general purpose user mobiledevice is configured to automatically scan on a communication stack fora physical address of a TV set.

53. The medium of embodiment 41, wherein the general purpose user mobiledevice receives user submitted TV parameters to scan for a TV set.

54. The medium of embodiment 41, further storing instructions to:

-   -   obtain an atmospherics data artifact from the atmospherics data        package.

55. The medium of embodiment 41, further storing instructions to:

-   -   extract user instant activities information based on analysis of        the atmospherics data artifact.

56. The medium of embodiment 55, further storing instructions to:

generate a user viewing status indication based on the user instantactivities information; and

incorporate the user viewing status indication into viewer measurementdata of the user selected channel

57. The medium of embodiment 41, wherein the received TV programschedule listing data comprises a plurality of ad tags.

58. The medium of embodiment 57, further storing instructions to:

-   -   retrieve an ad tag associated with the user selected media        program from the TV program schedule listing data.

59. The medium of embodiment 58, further storing instructions to:

extract key terms from the ad tags based by parsing ad contents;

query a survey question list based on the extracted key terms;

-   -   generate and sending a survey question from the query to the        user mobile device; and    -   obtain a user reaction to the survey question.

60. The medium of embodiment 57, further storing instructions to:

generate a social watching status message; and

populate the social watching status message to a social media platform.

Further embodiments of monitoring audience behavior of the ATMOS maycomprise the following:

1. A TV audience monitoring processor-implemented method, comprising:

providing TV program schedule listing data to a user mobile device;

receiving a user channel selection from the user mobile device;

receiving, from the user mobile device, an atmospherics data packageindicating user instant activity status;

obtaining an atmospherics data artifact from the atmospherics datapackage;

extracting user instant activities information based on analysis of theatmospherics data artifact;

generating a user viewing status indication based on the user instantactivities information; and

incorporating the user viewing status indication into viewer measurementdata of the user selected channel.

2. The method of embodiment 1, wherein the user mobile device comprisesany of a smartphone, a personal data assistant, a cellular phone, alaptop, and a tablet computer.

3. The method of embodiment 1, wherein the TV program schedule listingdata is transmitted via any of a cellar network, a 3G network, and aWifi network.

4. The method of embodiment 1, wherein the same user channel selectionis transmitted to a TV set via an infrared communication channel.

5. The method of embodiment 1, wherein the atmospherics data package iscaptured and aggregated by the user mobile device to monitor whether theuser is watching the selected channel.

6. The method of embodiment 1, further comprising determining a type ofthe atmospherics data artifact.

7. The method of embodiment 1, wherein the atmospherics data artifactcomprises an audio file.

8. The method of embodiment 7, further comprising:

-   -   determining audio content based on audio analysis.

9. The method of embodiment 8, further comprising:

-   -   when the audio content includes human voice, performing voice        recognition to determine whether a vocal source matches a        character in a TV show scheduled on the user selected channel;

10. The method of embodiment 8, further comprising:

-   -   when the audio content includes human voice, extracting key        terms from the human voice to determine whether the vocal        content is related to a TV show scheduled on the user selected        channel.

11. The method of 8, further comprising:

-   -   when the audio content includes ambient noise, determining an        audience environment status based on the noise level.

12. The method of embodiment 8, further comprising:

-   -   when the audio content includes media music, determine whether        the media music is related to a TV show scheduled on the user        selected channel.

13. The method of embodiment 1, wherein the atmospherics data artifactcomprises an image file.

14. The method of embodiment 1, further comprising:

determining graphic content based on image analysis.

15. The method of embodiment 14, further comprising:

when the graphic content comprises audience presence, performing facialrecognition to determine a number of presented audiences.

16. The method of embodiment 15, further comprising:

when the graphic content comprises a TV screen, determining whether theTV screen is related to a TV show scheduled on the user selectedchannel.

17. The method of embodiment 1, wherein the atmospherics data artifactcomprises GPS information.

18. The method of embodiment 1, further comprising:

determining an address type of the GPS information; and

determining whether the user is viewing TV based on the address type.

19. The method of embodiment 1, wherein the atmospherics data artifactcomprises a lighting sensing data file.

20. The method of embodiment 19, further comprising: determining whetheraudience environment is suitable for viewing based on the lightingsensing data.

21. The method of embodiment 1, wherein the atmospherics data artifactcomprises a device application activity log file.

22. The method of embodiment 21, further comprising:

determining a device application activity type; and

determining whether the user is viewing TV based on the deviceapplication activity type.

23. The method of embodiment 1, wherein the generating a user viewingstatus indication is performed based on a threshold-based progressiveprocedure.

24. The method of embodiment 23, wherein the threshold-based progressiveprocedure comprises:

analyzing a first atmospherics data artifact;

determining a first user activity indication based on the analysis;

assigning a first weight score value to the first atmospherics dataartifact;

determining whether the first weight score value exceeds a threshold;

if yes, determining the user is not watching the selected channel; and

if not, proceeding to analyzing a second atmospherics data artifact,

-   -   determining a second weight score value for the second        atmospherics data artifact,    -   generating an atmospherics score by adding the first weight        score value and the second score value, and    -   determining whether the atmospherics score exceeds the        threshold.

25. The method of embodiment 23, further comprising:

repeating analysis of atmospherics data artifacts when an accumulatedatmospherics score does not exceed the threshold.

26. The method of embodiment 23, further comprising:

determining the user is not watching the selected channel and exit thethreshold-based progressive procedure when the accumulated atmosphericsscore exceeds the threshold.

27. The method of embodiment 23, wherein the threshold-based progressiveprocedure analyzes atmospherics data artifacts based on complexity fromlow to high.

28. The method of embodiment 24, wherein the first atmospherics dataartifact comprises any of: GPS information and device applicationactivity status.

29. The method of embodiment 24, wherein the first and second weightscore values are retrieved from the pre-stored data table.

30. The method of embodiment 24, further comprising:

when the threshold-based progressive procedure determines the user isnot watching the selected channel,

-   -   excluding the user from being a viewer of the user selected        channel.

31. A TV audience monitoring system, comprising:

-   -   means to provide TV program schedule listing data to a user        mobile device;    -   means to receive a user channel selection from the user mobile        device;    -   means to receive, from the user mobile device, an atmospherics        data package indicating user instant activity status;    -   means to obtain an atmospherics data artifact from the        atmospherics data package;    -   means to extract user instant activities information based on        analysis of the atmospherics data artifact;    -   means to generate a user viewing status indication based on the        user instant activities information; and    -   means to incorporate the user viewing status indication into        viewer measurement data of the user selected channel.

32. The system of embodiment 31, wherein the user mobile devicecomprises any of a smartphone, a personal data assistant, a cellularphone, a laptop, and a tablet computer.

33. The system of embodiment 31, wherein the TV program schedule listingdata is transmitted via any of a cellar network, a 3G network, and aWifi network.

34. The system of embodiment 31, wherein the same user channel selectionis transmitted to a TV set via an infrared communication channel.

35. The system of embodiment 31, wherein the atmospherics data packageis captured and aggregated by the user mobile device to monitor whetherthe user is watching the selected channel.

36. The system of embodiment 31, further comprising determining a typeof the atmospherics data artifact.

37. The system of embodiment 31, wherein the atmospherics data artifactcomprises an audio file.

38. The system of embodiment 37, further comprising:

-   -   means to determine audio content based on audio analysis.

39. The system of embodiment 38, further comprising:

-   -   means to when the audio content includes human voice, perform        voice recognition to determine whether a vocal source matches a        character in a TV show scheduled on the user selected channel;

40. The system of embodiment 38, further comprising:

-   -   means to when the audio content includes human voice, extract        key terms from the human voice to determine whether the vocal        content is related to a TV show scheduled on the user selected        channel.

41. The system of 38, further comprising:

-   -   means to when the audio content includes ambient noise,        determine an audience environment status based on the noise        level.

42. The system of embodiment 38, further comprising:

-   -   means to when the audio content includes media music, determine        whether the media music is related to a TV show scheduled on the        user selected channel.

43. The system of embodiment 31, wherein the atmospherics data artifactcomprises an image file.

44. The system of embodiment 31, further comprising:

means to determine graphic content based on image analysis.

45. The system of embodiment 34, further comprising:

means to when the graphic content comprises audience presence, performfacial recognition to determine a number of presented audiences.

46. The system of embodiment 45, further comprising:

means to when the graphic content comprises a TV screen, determinewhether the TV screen is related to a TV show scheduled on the userselected channel.

47. The system of embodiment 31, wherein the atmospherics data artifactcomprises GPS information.

48. The system of embodiment 31, further comprising:

means to determine an address type of the GPS information; and

means to determine whether the user is viewing TV based on the addresstype.

49. The system of embodiment 31, wherein the atmospherics data artifactcomprises a lighting sensing data file.

50. The system of embodiment 49, further comprising: means to determinewhether audience environment is suitable for viewing based on thelighting sensing data.

51. The system of embodiment 31, wherein the atmospherics data artifactcomprises a device application activity log file.

52. The system of embodiment 41, further comprising:

means to determine a device application activity type; and means todetermine whether the user is viewing TV based on the device

application activity type.

53. The system of embodiment 31, wherein the generating a user viewingstatus indication is performed based on a threshold-based progressiveprocedure.

54. The system of embodiment 53, wherein the threshold-based progressiveprocedure comprises:

means to analyze a first atmospherics data artifact;

means to determine a first user activity indication based on theanalysis;

means to assign a first weight score value to the first atmosphericsdata artifact;

means to determine whether the first weight score value exceeds athreshold;

if yes, means to determine the user is not watching the selectedchannel; and

if not, means to proceed to analyzing a second atmospherics dataartifact,

-   -   means to determine a second weight score value for the second        atmospherics data artifact,    -   means to generate an atmospherics score by adding the first        weight score value and the second score value, and    -   means to determine whether the atmospherics score exceeds the        threshold.

55. The system of embodiment 53, further comprising:

means to repeat analysis of atmospherics data artifacts when anaccumulated atmospherics score does not exceed the threshold.

56. The system of embodiment 53, further comprising:

means to determine the user is not watching the selected channel andexit the threshold-based progressive procedure when the accumulatedatmospherics score exceeds the threshold.

57. The system of embodiment 53, wherein the threshold-based progressiveprocedure analyzes atmospherics data artifacts based on complexity fromlow to high.

58. The system of embodiment 54, wherein the first atmospherics dataartifact comprises any of: GPS information and device applicationactivity status.

59. The system of embodiment 54, wherein the first and second weightscore values are retrieved from the pre-stored data table.

60. The system of embodiment 54, further comprising:

when the threshold-based progressive procedure determines the user isnot watching the selected channel,

-   -   means to exclude the user from being a viewer of the user        selected channel.

61. A TV audience monitoring processor-readable non-transitory mediumstoring processor-executable instructions, said instructions issuable bya processor to:

-   -   provide TV program schedule listing data to a user mobile        device;    -   receive a user channel selection from the user mobile device;    -   receive, from the user mobile device, an atmospherics data        package indicating user instant activity status;    -   obtain an atmospherics data artifact from the atmospherics data        package;    -   extract user instant activities information based on analysis of        the atmospherics data artifact;    -   generate a user viewing status indication based on the user        instant activities information; and    -   incorporate the user viewing status indication into viewer        measurement data of the user selected channel.

62. The medium of embodiment 61, wherein the user mobile devicecomprises any of a smartphone, a personal data assistant, a cellularphone, a laptop, and a tablet computer.

63. The medium of embodiment 61, wherein the TV program schedule listingdata is transmitted via any of a cellar network, a 3G network, and aWifi network.

64. The medium of embodiment 61, wherein the same user channel selectionis transmitted to a TV set via an infrared communication channel.

65. The medium of embodiment 61, wherein the atmospherics data packageis captured and aggregated by the user mobile device to monitor whetherthe user is watching the selected channel.

66. The medium of embodiment 61, wherein the processor-executableinstructions are further issuable by the processor to determining a typeof the atmospherics data artifact.

67. The medium of embodiment 61, wherein the atmospherics data artifactcomprises an audio file.

68. The medium of embodiment 67, wherein the processor-executableinstructions are further issuable by the processor to:

-   -   determine audio content based on audio analysis.

69. The medium of embodiment 68, wherein the processor-executableinstructions are further issuable by the processor to:

-   -   when the audio content includes human voice, perform voice        recognition to determine whether a vocal source matches a        character in a TV show scheduled on the user selected channel;

70. The medium of embodiment 68, wherein the processor-executableinstructions are further issuable by the processor to:

-   -   when the audio content includes human voice, extract key terms        from the human voice to determine whether the vocal content is        related to a TV show scheduled on the user selected channel.

71. The medium of 68, wherein the processor-executable instructions arefurther issuable by the processor to:

-   -   when the audio content includes ambient noise, determine an        audience environment status based on the noise level.

72. The medium of embodiment 68, wherein the processor-executableinstructions are further issuable by the processor to:

-   -   when the audio content includes media music, determine whether        the media music is related to a TV show scheduled on the user        selected channel.

73. The medium of embodiment 61, wherein the atmospherics data artifactcomprises an image file.

74. The medium of embodiment 61, wherein the processor-executableinstructions are further issuable by the processor to:

-   -   determine graphic content based on image analysis.

75. The medium of embodiment 64, wherein the processor-executableinstructions are further issuable by the processor to:

-   -   when the graphic content comprises audience presence, perform        facial recognition to determine a number of presented audiences.

76. The medium of embodiment 75, wherein the processor-executableinstructions are further issuable by the processor to:

-   -   when the graphic content comprises a TV screen, determine        whether the TV screen is related to a TV show scheduled on the        user selected channel.

77. The medium of embodiment 61, wherein the atmospherics data artifactcomprises GPS information.

78. The medium of embodiment 61, wherein the processor-executableinstructions are further issuable by the processor to:

determine an address type of the GPS information; and

determine whether the user is viewing TV based on the address type.

79. The medium of embodiment 61, wherein the atmospherics data artifactcomprises a lighting sensing data file.

80. The medium of embodiment 79, wherein the processor-executableinstructions are further issuable by the processor to: determine whetheraudience environment is suitable for viewing based on the lightingsensing data.

81. The medium of embodiment 61, wherein the atmospherics data artifactcomprises a device application activity log file.

82. The medium of embodiment 71, wherein the processor-executableinstructions are further issuable by the processor to:

determine a device application activity type; and

determine whether the user is viewing TV based on the device applicationactivity type.

83. The medium of embodiment 61, wherein the generating a user viewingstatus indication is performed based on a threshold-based progressiveprocedure.

84. The medium of embodiment 83, wherein the threshold-based progressiveprocedure comprises:

analyze a first atmospherics data artifact;

determine a first user activity indication based on the analysis;

assign a first weight score value to the first atmospherics dataartifact;

determine whether the first weight score value exceeds a threshold;

if yes, determine the user is not watching the selected channel; and

if not, proceed to analyzing a second atmospherics data artifact,

-   -   determine a second weight score value for the second        atmospherics data artifact,    -   generate an atmospherics score by adding the first weight score        value and the second score value, and    -   determine whether the atmospherics score exceeds the threshold.

85. The medium of embodiment 83, wherein the processor-executableinstructions are further issuable by the processor to:

repeat analysis of atmospherics data artifacts when an accumulatedatmospherics score does not exceed the threshold.

86. The medium of embodiment 83, wherein the processor-executableinstructions are further issuable by the processor to:

determine the user is not watching the selected channel and exit thethreshold-based progressive procedure when the accumulated atmosphericsscore exceeds the threshold.

87. The medium of embodiment 83, wherein the threshold-based progressiveprocedure analyzes atmospherics data artifacts based on complexity fromlow to high.

88. The medium of embodiment 84, wherein the first atmospherics dataartifact comprises any of: GPS information and device applicationactivity status.

89. The medium of embodiment 84, wherein the first and second weightscore values are retrieved from the pre-stored data table.

90. The medium of embodiment 84, wherein the processor-executableinstructions are further issuable by the processor to:

when the threshold-based progressive procedure determines the user isnot watching the selected channel,

-   -   exclude the user from being a viewer of the user selected        channel.

Further embodiments of capturing audience atmospherics data at a usermobile device may comprise the following:

1. A TV audience monitoring processor-implemented method, comprising:

instantiating a TV mobile control component at a general purpose usermobile device;

receiving TV program schedule listing data at the general purpose usermobile device via a communication channel;

obtaining a user selection of TV program via a user interface of theinstantiated TV mobile control component;

transmitting a TV remote channel selection indication to a server viathe communication channel;

capturing atmospherics data indicative of user activities status at theuser mobile device;

generating an atmospherics data package comprising one or moreatmospherics data artifact from the captured atmospherics data; and

transmitting the generated atmospherics data package to the server.

2. The method of embodiment 1, wherein the user mobile device comprisesany of a smartphone, a personal data assistant, a cellular phone, alaptop, and a tablet computer.

3. The method of embodiment 1, wherein the communication networkcomprises any of a cellar network, a 3G network, and a Wifi network.

4. The method of embodiment 1, wherein the capturing atmospherics datais automatically performed by the TV mobile control component on aperiodic basis.

5. The method of embodiment 1, wherein the capturing atmospherics datais triggered by a user.

6. The method of embodiment 1, wherein the capturing atmospherics datacomprises snapping a photo by an image capturing component connected tothe user mobile device.

7. The method of embodiment 1, wherein the capturing atmospherics datacomprises obtaining a video clip by an image capturing componentconnected to the user mobile device.

8. The method of embodiment 1, wherein the capturing atmospherics datacomprises recording an audio clip by the user mobile device.

9. The method of embodiment 1, wherein the capturing atmospherics datacomprises obtaining GPS information of the user mobile device.

10. The method of embodiment 1, wherein the capturing atmospherics datacomprises obtaining lighting sensing data by the user mobile device.

ii. The method of embodiment 1, wherein the capturing atmospherics datacomprises obtaining device application activity status on the usermobile device.

12. The method of embodiment 1, wherein the atmospherics data artifactcomprises any of an image, a video clip, an audio clip, GPS information,device application data, and lighting data.

13. The method of embodiment 1, further comprising: prompting a requestto a user to position the user mobile device so that an image capturecomponent is focused on a TV screen.

14. The method of embodiment 6, wherein the snapped photo includes animage of audiences.

15. The method of embodiment 6, wherein the snapped photo includes a TVscreen.

16. The method of embodiment 8, wherein the audio clip includes ambientnoise of an environment.

17. The method of embodiment 8, wherein the audio clip includes recordedmedia sound.

18. The method of embodiment 8, wherein the audio clip includes humanvoices.

19. The method of embodiment 7, wherein the GPS information indicateswhether a user is located with a TV set.

20. The method of embodiment 10, wherein the lighting data indicateswhether amble light is provided to watch TV.

21. A TV audience monitoring apparatus, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

instantiate a TV mobile control component at a general purpose usermobile device;

receive TV program schedule listing data at the general purpose usermobile device via a communication channel;

obtain a user selection of TV program via a user interface of theinstantiated TV mobile control component;

transmit a TV remote channel selection indication to a server via thecommunication channel;

capture atmospherics data indicative of user activities status at theuser mobile device;

generate an atmospherics data package comprising one or moreatmospherics data artifact from the captured atmospherics data; and

transmit the generated atmospherics data package to the server.

22. The apparatus of embodiment 1, wherein the user mobile devicecomprises any of a smartphone, a personal data assistant, a cellularphone, a laptop, and a tablet computer.

23. The apparatus of embodiment 21, wherein the communication networkcomprises any of a cellar network, a 3G network, and a Wifi network.

24. The apparatus of embodiment 21, wherein the capturing atmosphericsdata is automatically performed by the TV mobile control component on aperiodic basis.

25. The apparatus of embodiment 21, wherein the capturing atmosphericsdata is triggered by a user.

26. The apparatus of embodiment 21, wherein the capturing atmosphericsdata comprises snapping a photo by an image capturing componentconnected to the user mobile device.

27. The apparatus of embodiment 21, wherein the capturing atmosphericsdata comprises obtaining a video clip by an image capturing componentconnected to the user mobile device.

28. The apparatus of embodiment 21, wherein the capturing atmosphericsdata comprises recording an audio clip by the user mobile device.

29. The apparatus of embodiment 21, wherein the capturing atmosphericsdata comprises obtaining GPS information of the user mobile device.

30. The apparatus of embodiment 21, wherein the capturing atmosphericsdata comprises obtaining lighting sensing data by the user mobiledevice.

31. The apparatus of embodiment 21, wherein the capturing atmosphericsdata comprises obtaining device application activity status on the usermobile device.

32. The apparatus of embodiment 21, wherein the atmospherics dataartifact comprises any of an image, a video clip, an audio clip, GPSinformation, device application data, and lighting data.

33. The apparatus of embodiment 21, wherein the processor further issuesinstructions to prompt a request to a user to position the user mobiledevice so that an image capture component is focused on a TV screen.

34. The apparatus of embodiment 26, wherein the snapped photo includesan image of audiences.

35. The apparatus of embodiment 26, wherein the snapped photo includes aTV screen.

36. The apparatus of embodiment 28, wherein the audio clip includesambient noise of an environment.

37. The apparatus of embodiment 28, wherein the audio clip includesrecorded media sound.

38. The apparatus of embodiment 28, wherein the audio clip includeshuman voices.

39. The apparatus of embodiment 29, wherein the GPS informationindicates whether a user is located with a TV set.

40. The apparatus of embodiment 30, wherein the lighting data indicateswhether amble light is provided to watch TV.

41. A TV audience monitoring processor-readable medium storingprocessor-executable instructions to:

instantiate a TV mobile control component at a general purpose usermobile device;

receive TV program schedule listing data at the general purpose usermobile device via a communication channel;

obtain a user selection of TV program via a user interface of theinstantiated TV mobile control component;

transmit a TV remote channel selection indication to a server via thecommunication channel;

capture atmospherics data indicative of user activities status at theuser mobile device;

generate an atmospherics data package comprising one or moreatmospherics data artifact from the captured atmospherics data; and

transmit the generated atmospherics data package to the server.

42. The medium of embodiment 41, wherein the user mobile devicecomprises any of a smartphone, a personal data assistant, a cellularphone, a laptop, and a tablet computer.

43. The medium of embodiment 41, wherein the communication networkcomprises any of a cellar network, a 3G network, and a Wifi network.

44. The medium of embodiment 41, wherein the capturing atmospherics datais automatically performed by the TV mobile control component on aperiodic basis.

45. The medium of embodiment 41, wherein the capturing atmospherics datais triggered by a user.

46. The medium of embodiment 41, wherein the capturing atmospherics datacomprises snapping a photo by an image capturing component connected tothe user mobile device.

47. The medium of embodiment 41, wherein the capturing atmospherics datacomprises obtaining a video clip by an image capturing componentconnected to the user mobile device.

48. The medium of embodiment 41, wherein the capturing atmospherics datacomprises recording an audio clip by the user mobile device.

49. The medium of embodiment 41, wherein the capturing atmospherics datacomprises obtaining GPS information of the user mobile device.

50. The medium of embodiment 41, wherein the capturing atmospherics datacomprises obtaining lighting sensing data by the user mobile device.

61. The medium of embodiment 41, wherein the capturing atmospherics datacomprises obtaining device application activity status on the usermobile device.

62. The medium of embodiment 41, wherein the atmospherics data artifactcomprises any of an image, a video clip, an audio clip, GPS information,device application data, and lighting data.

63. The medium of embodiment 41, further storing processor-executableinstructions to prompt a request to a user to position the user mobiledevice so that an image capture component is focused on a TV screen.

64. The medium of embodiment 56, wherein the snapped photo includes animage of audiences.

65. The medium of embodiment 56, wherein the snapped photo includes a TVscreen.

66. The medium of embodiment 58, wherein the audio clip includes ambientnoise of an environment.

67. The medium of embodiment 58, wherein the audio clip includesrecorded media sound.

68. The medium of embodiment 58, wherein the audio clip includes humanvoices.

69. The medium of embodiment 59, wherein the GPS information indicateswhether a user is located with a TV set.

60. The medium of embodiment 50, wherein the lighting data indicateswhether amble light is provided to watch TV.

Further embodiments of generating media content based surveyquestionnaires may comprise the following:

1. A media content based survey distribution and collectionprocessor-implemented method, comprising:

providing the obtained TV program schedule listing data including aplurality of ad tags to a user mobile device,

receiving a user media program selection message from the user mobiledevice;

retrieving an ad tag associated with the user selected media programfrom the TV program schedule listing data;

extracting key terms from the ad tags based by parsing ad contents;

querying a survey question list based on the extracted key terms;

generating and sending a survey question from the query to the usermobile device; and

obtaining a user reaction to the survey question.

2. The method of embodiment 1, wherein the ad tag is related to anadvertisement played during a commercial break associated with the userselected media program.

3. The method of embodiment 1, wherein the ad tag is related to anembedded ad placed in a scene of the user selected media program.

4. The method of embodiment 1, wherein the ad tag further comprises atimestamp of an ad, and information related to the advertised item.

5. The method of embodiment 1, further comprising:

determining a category of an advertised item based on the extracted keyterms.

6. The method of embodiment 5, further comprising:

retrieving survey questions from the survey question list based on thedetermined category.

7. The method of embodiment 1, wherein the generated survey question issent to the user mobile device shortly after a timestamp of the ad tag.

8. The method of embodiment 1, wherein the survey question issynchronized with the ad tag.

9. The method of embodiment 1, wherein the survey question comprises amultiple choice question.

10. The method of embodiment 1, wherein the survey question comprises aURL to a merchant shopping site.

ii. The method of embodiment 1, wherein the user reaction to the surveyquestion comprises a submission of answer to the survey question.

12. The method of embodiment 1, wherein the user reaction to the surveyquestion comprises a click on a URL provided in the survey question.

13. The method of embodiment 1, wherein the survey question is generatedbased on ad tags in a user's recent viewing history.

14. The method of embodiment 1, further comprising providing incentiverewards to a user after receiving an answer to the survey question.

15. The method of embodiment 1, further comprising analyzing ad deliveryand effects.

16. The method of embodiment 15, further comprising:

assigning a weighing score to the user reaction to the survey question.

17. The method of embodiment 16, wherein the weighing score isdetermined based on a type of the user reaction.

18. The method of embodiment 16, wherein the weighing score isdetermined based on a user's answer to the survey question.

19. The method of embodiment 15, further comprising: aggregatingweighing scores from a plurality of user reactions to determine adeffects.

20. The method of embodiment 15, further comprising: periodically updatethe analysis by combining newly received user reactions to surveyquestions.

21. A media content based survey distribution and collection system,comprising:

means for providing the obtained TV program schedule listing dataincluding a plurality of ad tags to a user mobile device,

means for receiving a user media program selection message from the usermobile device;

means for retrieving an ad tag associated with the user selected mediaprogram from the TV program schedule listing data;

means for extracting key terms from the ad tags based by parsing adcontents;

means for querying a survey question list based on the extracted keyterms;

means for generating and sending a survey question from the query to theuser mobile device; and

means for obtaining a user reaction to the survey question.

22. The system of embodiment 21, wherein the ad tag is related to anadvertisement played during a commercial break associated with the userselected media program.

23. The system of embodiment 21, wherein the ad tag is related to anembedded ad placed in a scene of the user selected media program.

24. The system of embodiment 21, wherein the ad tag further comprises atimestamp of an ad, and information related to the advertised item.

25. The system of embodiment 21, further comprising:

means for determining a category of an advertised item based on theextracted key terms.

26. The system of embodiment 5, further comprising:

means for retrieving survey questions from the survey question listbased on the determined category.

27. The system of embodiment 21, wherein the generated survey questionis sent to the user mobile device shortly after a timestamp of the adtag.

28. The system of embodiment 21, wherein the survey question issynchronized with the ad tag.

29. The system of embodiment 21, wherein the survey question comprises amultiple choice question.

30. The system of embodiment 21, wherein the survey question comprises aURL to a merchant shopping site.

31. The system of embodiment 21, wherein the user reaction to the surveyquestion comprises a submission of answer to the survey question.

32. The system of embodiment 21, wherein the user reaction to the surveyquestion comprises a click on a URL provided in the survey question.

33. The system of embodiment 21, wherein the survey question isgenerated based on ad tags in a user's recent viewing history.

34. The system of embodiment 21, further comprising means for providingincentive rewards to a user after receiving an answer to the surveyquestion.

35. The system of embodiment 21, further comprising means for analyzingad delivery and effects.

36. The system of embodiment 35, further comprising:

means for assigning a weighing score to the user reaction to the surveyquestion.

37. The system of embodiment 36, wherein the weighing score isdetermined based on a type of the user reaction.

38. The system of embodiment 36, wherein the weighing score isdetermined based on a user's answer to the survey question.

39. The system of embodiment 35, further comprising: means foraggregating weighing scores from a plurality of user reactions todetermine ad effects.

40. The system of embodiment 35, further comprising: means forperiodically updating the analysis by combining newly received userreactions to survey questions.

41. A media content based survey distribution and collectionprocessor-readable non-transitory medium storing processor-executableinstructions to:

provide the obtained TV program schedule listing data including aplurality of ad tags to a user mobile device,

receive a user media program selection message from the user mobiledevice;

retrieve an ad tag associated with the user selected media program fromthe TV program schedule listing data;

extract key terms from the ad tags based by parsing ad contents;

query a survey question list based on the extracted key terms;

generate and send a survey question from the query to the user mobiledevice; and

obtain a user reaction to the survey question.

42. The medium of embodiment 21, wherein the ad tag is related to anadvertisement played during a commercial break associated with the userselected media program.

43. The medium of embodiment 21, wherein the ad tag is related to anembedded ad placed in a scene of the user selected media program.

44. The medium of embodiment 21, wherein the ad tag further comprises atimestamp of an ad, and information related to the advertised item.

45. The medium of embodiment 21, further comprising:

means for determining a category of an advertised item based on theextracted key terms.

46. The medium of embodiment 45, further storing processor-executableinstructions to retrieve survey questions from the survey question listbased on the determined category.

47. The medium of embodiment 41, wherein the generated survey questionis sent to the user mobile device shortly after a timestamp of the adtag.

48. The medium of embodiment 41, wherein the survey question issynchronized with the ad tag.

49. The medium of embodiment 41, wherein the survey question comprises amultiple choice question.

50. The medium of embodiment 41, wherein the survey question comprises aURL to a merchant shopping site.

51. The medium of embodiment 41, wherein the user reaction to the surveyquestion comprises a submission of answer to the survey question.

52. The medium of embodiment 41, wherein the user reaction to the surveyquestion comprises a click on a URL provided in the survey question.

53. The medium of embodiment 41, wherein the survey question isgenerated based on ad tags in a user's recent viewing history.

54. The medium of embodiment 41, further storing processor-executableinstructions to provide incentive rewards to a user after receiving ananswer to the survey question.

55. The medium of embodiment 41, further storing processor-executableinstructions to analyze ad delivery and effects.

56. The medium of embodiment 55, further storing processor-executableinstructions to assign a weighing score to the user reaction to thesurvey question.

57. The medium of embodiment 56, wherein the weighing score isdetermined based on a type of the user reaction.

58. The medium of embodiment 56, wherein the weighing score isdetermined based on a user's answer to the survey question.

59. The medium of embodiment 55, further storing processor-executableinstructions to aggregate weighing scores from a plurality of userreactions to determine ad effects.

60. The medium of embodiment 55, further storing processor-executableinstructions to periodically update the analysis by combining newlyreceived user reactions to survey questions.

Further embodiments of generating synchronized media content basedproduct placement ads may comprise the following:

1. A media content based advertising processor-implemented method,comprising:

providing TV program schedule listing data including a plurality of adtags to a user mobile device,

receiving a user media program selection message from the user mobiledevice;

retrieving an ad tag associated with the user selected media programfrom the TV program schedule listing data;

identifying an advertised item embedded in the media program based onthe retrieved ad tag;

determining an available ad template associated with the retrieved adtag;

generating an ad for the embedded advertised item based on the availablead template; and

providing the generated ad to the user mobile device based on atimestamp of the ad tag.

2. The method of embodiment 1, wherein the ad tag is related to anadvertisement played during a commercial break associated with the userselected media program.

3. The method of embodiment 1, wherein the ad tag is related to anembedded ad placed in a scene of the user selected media program.

4. The method of embodiment 1, wherein the ad tag further comprises thetimestamp of an ad, and information related to the advertised item.

5. The method of embodiment 1, wherein the available ad templatecomprises a static ad template.

6. The method of embodiment 5, further comprising populating informationof the identified advertised item into the static ad template.

7. The method of embodiment 1, wherein the available ad templatecomprises an image captured from the media program, and said imagecomprises the identified advertised item.

8. The method of embodiment 7, further comprising generating aninteractive ad using the available ad template.

9. The method of embodiment 7, wherein the image comprises an indiciabox indicating the identified advertised item.

10. The method of embodiment 8, wherein the interactive ad comprises arating of the identified advertised item.

11. The method of embodiment 8, wherein the interactive ad comprises animmediate purchasing option including a URL to a merchant shopping site.

12. The method of embodiment 1, wherein the interactive ad comprises anoption for a user to enter a rating for the identified advertised item.

13. The method of embodiment 1, further comprising: providing options toa user to browse interactive ads.

14. The method of embodiment 13, wherein the options comprises: browsinginteractive ads by any of: character, item category, season, episode.

15. The method of embodiment 1, further comprising:

receiving a user interaction with the generated ad; and

analyzing ad delivery and effects.

16. The method of embodiment 15, further comprising:

assigning a weighing score to the user interaction.

17. The method of embodiment 16, wherein the weighing score isdetermined based on a type of the user interaction.

18. The method of embodiment 16, wherein the user interaction comprisesany of: entry of product rating, click to view more, and click topurchase.

19. The method of embodiment 15, further comprising: aggregatingweighing scores from a plurality of user reactions to determine adeffects.

20. The method of embodiment 15, further comprising: periodically updatethe analysis by combining newly received user interactions.

21. A media content based advertising system, comprising:

means for providing TV program schedule listing data including aplurality of ad tags to a user mobile device,

means for receiving a user media program selection message from the usermobile device;

means for retrieving an ad tag associated with the user selected mediaprogram from the TV program schedule listing data;

means for identifying an advertised item embedded in the media programbased on the retrieved ad tag;

means for determining an available ad template associated with theretrieved ad tag;

means for generating an ad for the embedded advertised item based on theavailable ad template; and

means for providing the generated ad to the user mobile device based ona timestamp of the ad tag.

22. The system of embodiment 21, wherein the ad tag is related to anadvertisement played during a commercial break associated with the userselected media program.

23. The system of embodiment 21, wherein the ad tag is related to anembedded ad placed in a scene of the user selected media program.

24. The system of embodiment 21, wherein the ad tag further comprisesthe timestamp of an ad, and information related to the advertised item.

25. The system of embodiment 21, wherein the available ad templatecomprises a static ad template.

26. The system of embodiment 25, further comprising populatinginformation of the identified advertised item into the static adtemplate.

27. The system of embodiment 21, wherein the available ad templatecomprises an image captured from the media program, and said imagecomprises the identified advertised item.

28. The system of embodiment 27, further comprising means for generatingan interactive ad using the available ad template.

29. The system of embodiment 27, wherein the image comprises an indiciabox indicating the identified advertised item.

30. The system of embodiment 28, wherein the interactive ad comprises arating of the identified advertised item.

31. The system of embodiment 28, wherein the interactive ad comprises animmediate purchasing option including a URL to a merchant shopping site.

32. The system of embodiment 21, wherein the interactive ad comprises anoption for a user to enter a rating for the identified advertised item.

33. The system of embodiment 21, further comprising: providing optionsto a user to browse interactive ads.

34. The system of embodiment 33, wherein the options comprises: browsinginteractive ads by any of: character, item category, season, episode.

35. The system of embodiment 21, further comprising:

receiving a user interaction with the generated ad; and

analyzing ad delivery and effects.

36. The system of embodiment 35, further comprising:

assigning a weighing score to the user interaction.

37. The system of embodiment 36, wherein the weighing score isdetermined based on a type of the user interaction.

38. The system of embodiment 36, wherein the user interaction comprisesany of: entry of product rating, click to view more, and click topurchase.

39. The system of embodiment 35, further comprising: means foraggregating weighing scores from a plurality of user reactions todetermine ad effects.

40. The system of embodiment 35, further comprising: means forperiodically updating the analysis by combining newly received userinteractions.

41. A media content based advertising processor-readable non-transitorymedium storing processor-executable instructions to:

provide TV program schedule listing data including a plurality of adtags to a user mobile device,

receive a user media program selection message from the user mobiledevice;

retrieve an ad tag associated with the user selected media program fromthe TV program schedule listing data;

identify an advertised item embedded in the media program based on theretrieved ad tag;

determine an available ad template associated with the retrieved ad tag;

generate an ad for the embedded advertised item based on the availablead template; and

provide the generated ad to the user mobile device based on a timestampof the ad tag.

42. The medium of embodiment 41, wherein the ad tag is related to anadvertisement played during a commercial break associated with the userselected media program.

43. The medium of embodiment 41, wherein the ad tag is related to anembedded ad placed in a scene of the user selected media program.

44. The medium of embodiment 41, wherein the ad tag further comprisesthe timestamp of an ad, and information related to the advertised item.

45. The medium of embodiment 41, wherein the available ad templatecomprises a static ad template.

46. The medium of embodiment 45, further storing instructions topopulate information of the identified advertised item into the staticad template.

47. The medium of embodiment 41, wherein the available ad templatecomprises an image captured from the media program, and said imagecomprises the identified advertised item.

48. The medium of embodiment 47, further storing instructions togenerate an interactive ad using the available ad template.

49. The medium of embodiment 47, wherein the image comprises an indiciabox indicating the identified advertised item.

50. The medium of embodiment 48, wherein the interactive ad comprises arating of the identified advertised item.

51. The medium of embodiment 48, wherein the interactive ad comprises animmediate purchasing option including a URL to a merchant shopping site.

52. The medium of embodiment 41, wherein the interactive ad comprises anoption for a user to enter a rating for the identified advertised item.

53. The medium of embodiment 41, further storing instructions to provideoptions to a user to browse interactive ads.

54. The medium of embodiment 43, wherein the options comprises: browsinginteractive ads by any of: character, item category, season, episode.

55. The medium of embodiment 41, further storing instructions to:

receive a user interaction with the generated ad; and

analyze ad delivery and effects.

56. The medium of embodiment 55, further storing instructions to:

assign a weighing score to the user interaction.

57. The medium of embodiment 56, wherein the weighing score isdetermined based on a type of the user interaction.

58. The medium of embodiment 57, wherein the user interaction comprisesany of: entry of product rating, click to view more, and click topurchase.

59. The medium of embodiment 55, further comprising: aggregatingweighing scores from a plurality of user reactions to determine adeffects.

60. The medium of embodiment 55, further comprising: periodically updatethe analysis by combining newly received user interactions.

Further embodiments of social content access may comprise the following:

1. A social media content access processor-implemented method,comprising:

identifying a request to access user social media content;

obtaining user authorization credentials to access user social mediacontent;

sending an access request with the obtained user authorizationcredentials to a social media platform;

receiving social media content data from the social media platform;

determining a type of the received media content data;

tagging the received media content data based on the type according to aprogressive taxonomy mechanism;

receive a social media analytics request for an item;

querying the tagged media content data based on key terms related to theitem; and

determining impression heuristics for the item based on query results.

2. The method of embodiment 1, wherein the request to access socialmedia content comprises a request received from a user to populate asocial watching event status to a social media platform.

3. The method of embodiment 1, wherein the request to access socialmedia content comprises a periodic social media content update.

4. The method of embodiment 1, wherein the request to access socialmedia content is triggered by an obtained request for social mediaanalytics.

5. The method of embodiment 1, wherein the obtaining user authorizationcredentials comprises:

prompting a user to provide social media login credentials.

6. The method of embodiment 1, wherein the obtaining user authorizationcredentials comprises: redirecting a user to a social media login page.

7. The method of embodiment 1, wherein the social media platform obtainsa user application ID and user permission.

8. The method of embodiment 1, wherein the user authorizationcredentials comprise a user token received from the social mediaplatform.

9. The method of embodiment 1, wherein the receiving social mediacontent data from the social media platform is scheduled on a periodicbasis.

10. The method of embodiment 1, wherein the receiving social mediacontent data from the social media platform is performed on demand.

11. The method of embodiment 1, wherein the type of the received mediacontent data comprises any of structured data and unstructured data.

12. The method of embodiment ii, wherein the structured data comprisesany of a number of user social media connections and a user profile.

13. The method of embodiment ii, wherein the unstructured data comprisesraw texts of social media comments.

14. The method of embodiment ii, wherein the tagging the received mediacontent data comprises tagging unstructured data based on category ofdata content.

15. The method of embodiment 1, wherein progressive taxonomy mechanismcomprises a set of pre-determined key terms.

16. The method of embodiment 15, further comprising:

querying the social media content based on a key term; and

tagging the social media content with the key term when the query findssuch key term.

17. The method of embodiment 15, further comprising:

perform text analytics on the social media content.

18. The method of embodiment 1, wherein the social media analyticsrequest comprises an impression request of the item.

19. The method of embodiment 1, further comprising: determining a keyword for the item.

20. The method of embodiment 1, wherein the item comprises a TV show.

21. The method of embodiment 1, wherein the item comprises a brand nameproduct.

22. The method of embodiment 19, further comprising: determining whethertags of unstructured data includes the key word.

23. The method of embodiment 1, wherein the determining impressionheuristics comprises assigning a weight value to the social mediacontent based on the progressive mechanism.

24. The method of embodiment 1, wherein the determining impressionheuristics of the item based on query results comprises calculating animpression score.

25. The method of embodiment 1, wherein the impression heuristics isdetermined based on statistical analysis of social media content.

26. A social media content access processor-implemented system,comprising:

means for identifying a request to access user social media content;

means for obtaining user authorization credentials to access user socialmedia content;

means for sending an access request with the obtained user authorizationcredentials to a social media platform;

means for receiving social media content data from the social mediaplatform;

means for determining a type of the received media content data;

means for tagging the received media content data based on the typeaccording to a progressive taxonomy mechanism;

means for receiving a social media analytics request for an item;

means for querying the tagged media content data based on key termsrelated to the item; and

means for determining impression heuristics for the item based on queryresults.

27. The system of embodiment 26, wherein the request to access socialmedia content comprises a request received from a user to populate asocial watching event status to a social media platform.

28. The system of embodiment 26, wherein the request to access socialmedia content comprises a periodic social media content update.

29. The system of embodiment 26, wherein the request to access socialmedia content is triggered by an obtained request for social mediaanalytics.

30. The system of embodiment 26, wherein the obtaining userauthorization credentials comprises:

prompting a user to provide social media login credentials.

31. The system of embodiment 26, wherein the obtaining userauthorization credentials comprises: redirecting a user to a socialmedia login page.

32. The system of embodiment 26, wherein the social media platformobtains a user application ID and user permission.

33. The system of embodiment 26, wherein the user authorizationcredentials comprise a user token received from the social mediaplatform.

34. The system of embodiment 26, wherein the receiving social mediacontent data from the social media platform is scheduled on a periodicbasis.

35. The system of embodiment 26, wherein the receiving social mediacontent data from the social media platform is performed on demand.

36. The system of embodiment 26, wherein the type of the received mediacontent data comprises any of structured data and unstructured data.

37. The system of embodiment 36, wherein the structured data comprisesany of a number of user social media connections and a user profile.

38. The system of embodiment 36, wherein the unstructured data comprisesraw texts of social media comments.

39. The system of embodiment 36, wherein the tagging the received mediacontent data comprises tagging unstructured data based on category ofdata content.

40. The system of embodiment 26, wherein progressive taxonomy mechanismcomprises a set of pre-determined key terms.

41. The system of embodiment 26, further comprising:

means for querying the social media content based on a key term; and

means for tagging the social media content with the key term when thequery finds such key term.

42. The system of embodiment 26, further comprising:

means for performing text analytics on the social media content.

43. The system of embodiment 26, wherein the social media analyticsrequest comprises an impression request of the item.

44. The system of embodiment 26, further comprising: means fordetermining a key word for the item.

45. The system of embodiment 26, wherein the item comprises a TV show.

46. The system of embodiment 26, wherein the item comprises a brand nameproduct.

47. The system of embodiment 26, further comprising: means fordetermining whether tags of unstructured data includes the key word.

48. The system of embodiment 26, wherein the determining impressionheuristics comprises assigning a weight value to the social mediacontent based on the progressive mechanism.

49. The system of embodiment 26, wherein the determining impressionheuristics of the item based on query results comprises calculating animpression score.

50. The system of embodiment 26, wherein the impression heuristics isdetermined based on statistical analysis of social media content.

51. A social media content access processor-readable non-transitorymedium storing processor-executable instructions to:

identify a request to access user social media content;

obtain user authorization credentials to access user social mediacontent;

send an access request with the obtained user authorization credentialsto a social media platform;

receive social media content data from the social media platform;

determine a type of the received media content data;

tag the received media content data based on the type according to aprogressive taxonomy mechanism;

receive a social media analytics request for an item;

query the tagged media content data based on key terms related to theitem; and

determine impression heuristics for the item based on query results.

52. The medium of embodiment 51, wherein the request to access socialmedia content comprises a request received from a user to populate asocial watching event status to a social media platform.

53. The medium of embodiment 51, wherein the request to access socialmedia content comprises a periodic social media content update.

54. The medium of embodiment 51, wherein the request to access socialmedia content is triggered by an obtained request for social mediaanalytics.

55. The medium of embodiment 51, wherein the obtaining userauthorization credentials comprises:

prompting a user to provide social media login credentials.

56. The medium of embodiment 51, wherein the obtaining userauthorization credentials comprises: redirecting a user to a socialmedia login page.

57. The medium of embodiment 51, wherein the social media platformobtains a user application ID and user permission.

58. The medium of embodiment 51, wherein the user authorizationcredentials comprise a user token received from the social mediaplatform.

59. The medium of embodiment 51, wherein the receiving social mediacontent data from the social media platform is scheduled on a periodicbasis.

60. The medium of embodiment 51, wherein the receiving social mediacontent data from the social media platform is performed on demand.

61. The medium of embodiment 51, wherein the type of the received mediacontent data comprises any of structured data and unstructured data.

62. The medium of embodiment 61, wherein the structured data comprisesany of a number of user social media connections and a user profile.

63. The medium of embodiment 61, wherein the unstructured data comprisesraw texts of social media comments.

64. The medium of embodiment 61, wherein the tagging the received mediacontent data comprises tagging unstructured data based on category ofdata content.

65. The medium of embodiment 51, wherein progressive taxonomy mechanismcomprises a set of pre-determined key terms.

66. The medium of embodiment 65, further comprising:

querying the social media content based on a key term; and

tagging the social media content with the key term when the query findssuch key term.

67. The medium of embodiment 65, further comprising:

perform text analytics on the social media content.

68. The medium of embodiment 51, wherein the social media analyticsrequest comprises an impression request of the item.

69. The medium of embodiment 51, further comprising: determining a keyword for the item.

70. The medium of embodiment 51, wherein the item comprises a TV show.

71. The medium of embodiment 51, wherein the item comprises a brand nameproduct.

72. The medium of embodiment 69, further comprising: determining whethertags of unstructured data includes the key word.

73. The medium of embodiment 51, wherein the determining impressionheuristics comprises assigning a weight value to the social mediacontent based on the progressive mechanism.

74. The medium of embodiment 51, wherein the determining impressionheuristics of the item based on query results comprises calculating animpression score.

75. The medium of embodiment 51, wherein the impression heuristics isdetermined based on statistical analysis of social media content.

Further embodiments of cross media channel analytics may comprise thefollowing:

1. A media analytics processor-implemented method, comprising:

receiving a user impression media analytics request including anidentified object;

obtaining media analytics parameters from a user interface;

obtaining user authorization credentials for accessing user mediaexposure data;

obtaining user media exposure data from a variety of data channels uponverification of the obtained user authorization credentials;

querying for user impression data related to the identified object fromthe obtained user media exposure data;

classifying the queried user impression data related to the identifiedobject based on different classification measures; and

generating user impression reports for the identified object.

2. The method of embodiment 1, wherein the identified object comprises aTV show.

3. The method of embodiment 1, wherein the identified object comprises abrand name.

4. The method of embodiment 1, wherein the media analytics parameterscomprise any of a TV network, a TV show genre, a TV show name.

5. The method of embodiment 1, wherein the media analytics parametersfurther comprise user gender, age group, user interface types, userphone type, day part time range and user location.

6. The method of embodiment 1, wherein the media analytics parametersfurther comprise types of social media platform.

7. The method of embodiment 1, wherein the obtaining user authorizationcredentials comprises recruiting social media users to share socialcontent.

8. The method of embodiment 1, wherein the obtaining user authorizationcredentials further comprises prompting a user to provide social medialogin credentials.

9. The method of embodiment 1, wherein the obtaining user authorizationcredentials further comprises redirecting a user to a social media loginpage.

10. The method of embodiment 1, wherein the user media exposure datacomprises any of user website visits, social media content and TVviewing data.

11. The method of embodiment 1, wherein the variety of data channelscomprise a mobile meter.

12. The method of embodiment 1, wherein the variety of data channelscomprise a mobile application instantiated on a user mobile device.

13. The method of embodiment 1, wherein the variety of data channelscomprise social media.

14. The method of embodiment 1, wherein the variety of data channelscomprise flash/HTTP cookies.

15. The method of embodiment 1, wherein the user media exposure datafurther comprises user responses to survey questions, GPS locations,user application usage, and mobile search behavior.

16. The method of embodiment 1, wherein the querying for user impressiondata comprises a progressive search based on the identified objectrelated key terms.

17. The method of embodiment 1, wherein the classification measurescomprise any of user gender, user age group, day of week, and day parttime range.

18. The method of embodiment 1, wherein the user impression reports areclassified by the classification measures.

19. The method of embodiment 1, wherein the user impression reportscomprise application data statistics as to any of: number of totalapplication sessions, media session length, number of total users.

20. The method of embodiment 1, wherein the user impression reportsfurther comprise any of unique users, percent active reach, time perperson, number of visits, number of pages viewed, visits/person, usergender, and age groups.

21. A media analytics system, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

receive a user impression media analytics request including anidentified object;

obtain media analytics parameters from a user interface;

obtain user authorization credentials for accessing user media exposuredata;

obtain user media exposure data from a variety of data channels uponverification of the obtained user authorization credentials;

query for user impression data related to the identified object from theobtained user media exposure data;

classify the queried user impression data related to the identifiedobject based on different classification measures; and

generate user impression reports for the identified object.

22. The system of embodiment 21, wherein the identified object comprisesa TV show.

23. The system of embodiment 21, wherein the identified object comprisesa brand name.

24. The system of embodiment 21, wherein the media analytics parameterscomprise any of a TV network, a TV show genre, a TV show name.

25. The system of embodiment 21, wherein the media analytics parametersfurther comprise user gender, age group, user interface types, userphone type, day part time range and user location.

26. The system of embodiment 21, wherein the media analytics parametersfurther comprise types of social media platform.

27. The system of embodiment 21, wherein the obtaining userauthorization credentials comprises recruiting social media users toshare social content.

28. The system of embodiment 21, wherein the obtaining userauthorization credentials further comprises prompting a user to providesocial media login credentials.

29. The system of embodiment 21, wherein the obtaining userauthorization credentials further comprises redirecting a user to asocial media login page.

30. The system of embodiment 21, wherein the user media exposure datacomprises any of user website visits, social media content and TVviewing data.

31. The system of embodiment 21, wherein the variety of data channelscomprise a mobile meter.

32. The system of embodiment 21, wherein the variety of data channelscomprise a mobile application instantiated on a user mobile device.

33. The system of embodiment 21, wherein the variety of data channelscomprise social media.

34. The system of embodiment 21, wherein the variety of data channelscomprise flash/HTTP cookies.

35. The system of embodiment 21, wherein the user media exposure datafurther comprises user responses to survey questions, GPS locations,user application usage, and mobile search behavior.

36. The system of embodiment 21, wherein the querying for userimpression data comprises a progressive search based on the identifiedobject related key terms.

37. The system of embodiment 21, wherein the classification measurescomprise any of user gender, user age group, day of week, and day parttime range.

38. The system of embodiment 21, wherein the user impression reports areclassified by the classification measures.

39. The system of embodiment 21, wherein the user impression reportscomprise application data statistics as to any of: number of totalapplication sessions, media session length, number of total users.

40. The system of embodiment 21, wherein the user impression reportsfurther comprise any of unique users, percent active reach, time perperson, number of visits, number of pages viewed, visits/person, usergender, and age groups.

41. A media analytics processor-readable storage medium storingprocessor-executable instructions to:

receive a user impression media analytics request including anidentified object;

obtain media analytics parameters from a user interface;

obtain user authorization credentials for accessing user media exposuredata;

obtain user media exposure data from a variety of data channels uponverification of the obtained user authorization credentials;

query for user impression data related to the identified object from theobtained user media exposure data;

classify the queried user impression data related to the identifiedobject based on different classification measures; and

generate user impression reports for the identified object.

42. The medium of embodiment 41, wherein the identified object comprisesa TV show.

43. The medium of embodiment 41, wherein the identified object comprisesa brand name.

44. The medium of embodiment 41, wherein the media analytics parameterscomprise any of a TV network, a TV show genre, a TV show name.

45. The medium of embodiment 41, wherein the media analytics parametersfurther comprise user gender, age group, user interface types, userphone type, day part time range and user location.

46. The medium of embodiment 41, wherein the media analytics parametersfurther comprise types of social media platform.

47. The medium of embodiment 41, wherein the obtaining userauthorization credentials comprises recruiting social media users toshare social content.

48. The medium of embodiment 41, wherein the obtaining userauthorization credentials further comprises prompting a user to providesocial media login credentials.

49. The medium of embodiment 41, wherein the obtaining userauthorization credentials further comprises redirecting a user to asocial media login page.

40. The medium of embodiment 41, wherein the user media exposure datacomprises any of user website visits, social media content and TVviewing data.

51. The medium of embodiment 41, wherein the variety of data channelscomprise a mobile meter.

52. The medium of embodiment 41, wherein the variety of data channelscomprise a mobile application instantiated on a user mobile device.

53. The medium of embodiment 41, wherein the variety of data channelscomprise social media.

54. The medium of embodiment 41, wherein the variety of data channelscomprise flash/HTTP cookies.

55. The medium of embodiment 41, wherein the user media exposure datafurther comprises user responses to survey questions, GPS locations,user application usage, and mobile search behavior.

56. The medium of embodiment 41, wherein the querying for userimpression data comprises a progressive search based on the identifiedobject related key terms.

57. The medium of embodiment 41, wherein the classification measurescomprise any of user gender, user age group, day of week, and day parttime range.

58. The medium of embodiment 41, wherein the user impression reports areclassified by the classification measures.

59. The medium of embodiment 41, wherein the user impression reportscomprise application data statistics as to any of: number of totalapplication sessions, media session length, number of total users.

60. The medium of embodiment 41, wherein the user impression reportsfurther comprise any of unique users, percent active reach, time perperson, number of visits, number of pages viewed, visits/person, usergender, and age groups.

Further embodiments of mobile data tracking may comprise the following:

1. A mobile content tracking and analyzing processor-implemented method,comprising:

obtaining a user mobile device identifier;

monitoring data traffic coming in and out of the user mobile devicebased on the obtained user mobile device identifier via a mobile usagetracking entity;

parsing the monitored data traffic to determine a data content type ofthe data traffic;

determining user media content exposure information from the parsedmonitored data traffic based on the data content type; and

generating user media content exposure statistics data.

2. The method of embodiment 1, wherein the user mobile device identifiercomprises a hardware identifier.

3. The method of embodiment 1, wherein the user mobile device identifiercomprises a physical address.

4. The method of embodiment 1, wherein the data traffic is obtained viaa mobile application instantiated on the user mobile device.

5. The method of embodiment 1, wherein the monitoring data traffic isobtained via a mobile meter.

6. The method of embodiment 1, wherein the determine a data content typeof the data traffic comprises extracting a data type filed value from adata event message.

7. The method of embodiment 1, wherein the data content type comprisesany of:

a URL link, application information, media usage data, survey responsedata, and social data.

8. The method of embodiment 1, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises a URL link; and

determining the URL link includes an advertisement.

9. The method of embodiment 8, wherein the determining user mediacontent exposure information further comprises:

determining a classification of the advertisement;

obtaining identifying information of the advertisement; and

storing the identifying information with the user as advertisementexposure information.

10. The method of embodiment 1, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises applicationinformation;

determining device application inventory; and

obtaining application group sharing information

11. The method of embodiment 10, wherein the application group sharinginformation comprises a list of social connections.

12. The method of embodiment 1, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises media usageinformation; and

determining a media title.

13. The method of embodiment 1, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises social content; and

processing the social content to extract user impression indication.

14. The method of embodiment 13, wherein the user impression indicationis related to a product name.

15. The method of embodiment 1, wherein the data traffic is monitored ata proxy server.

16. The method of embodiment 1, wherein the monitored data trafficfurther comprises any of:

TV channel changing events, mobile advertising data, mobile applicationusage data, social media profile, social media comments, and websitevisits.

17. The method of embodiment 1, wherein the user media content exposurestatistics data comprises any of user brand impression measures.

18. The method of embodiment 17, wherein the user brand impressionmeasures comprises a list of top mentioned brands.

19. The method of embodiment 1, further comprising:

providing and generating individualized ad contents to the user mobiledevice based on the generated user media content exposure statisticsdata.

20. The method of embodiment 1, further comprising:

generating individualized survey questions to the user mobile devicebased on the generated user media content exposure statistics data.

21. A mobile content tracking and analyzing system, comprising:

a memory;

a processor disposed in communication with said memory, and configuredto issue a plurality of processing instructions stored in the memory,wherein the processor issues instructions to:

obtain a user mobile device identifier;

monitor data traffic coming in and out of the user mobile device basedon the obtained user mobile device identifier via a mobile usagetracking entity;

parse the monitored data traffic to determine a data content type of thedata traffic;

determine user media content exposure information from the parsedmonitored data traffic based on the data content type; and

generate user media content exposure statistics data.

22. The system of embodiment 21, wherein the user mobile deviceidentifier comprises a hardware identifier.

23. The system of embodiment 21, wherein the user mobile deviceidentifier comprises a physical address.

24. The system of embodiment 21, wherein the data traffic is obtainedvia a mobile application instantiated on the user mobile device.

25. The system of embodiment 21, wherein the monitoring data traffic isobtained via a mobile meter.

26. The system of embodiment 21, wherein the determine a data contenttype of the data traffic comprises extracting a data type filed valuefrom a data event message.

27. The system of embodiment 21, wherein the data content type comprisesany of:

a URL link, application information, media usage data, survey responsedata, and social data.

28. The system of embodiment 21, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises a URL link; and

determining the URL link includes an advertisement.

29. The system of embodiment 28, wherein the determining user mediacontent exposure information further comprises:

determining a classification of the advertisement;

obtaining identifying information of the advertisement; and

storing the identifying information with the user as advertisementexposure information.

30. The system of embodiment 21, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises applicationinformation;

determining device application inventory; and

obtaining application group sharing information

31. The system of embodiment 30, wherein the application group sharinginformation comprises a list of social connections.

32. The system of embodiment 21, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises media usageinformation; and

determining a media title.

33. The system of embodiment 21, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises social content; and

processing the social content to extract user impression indication.

34. The system of embodiment 33, wherein the user impression indicationis related to a product name.

35. The system of embodiment 21, wherein the data traffic is monitoredat a proxy server.

36. The system of embodiment 21, wherein the monitored data trafficfurther comprises any of:

TV channel changing events, mobile advertising data, mobile applicationusage data, social media profile, social media comments, and websitevisits.

37. The system of embodiment 21, wherein the user media content exposurestatistics data comprises any of user brand impression measures.

38. The system of embodiment 37, wherein the user brand impressionmeasures comprises a list of top mentioned brands.

39. The system of embodiment 21, further comprising:

providing and generating individualized ad contents to the user mobiledevice based on the generated user media content exposure statisticsdata.

40. The system of embodiment 21, further comprising:

generating individualized survey questions to the user mobile devicebased on the generated user media content exposure statistics data.

41. A mobile content tracking and analyzing processor-implementedstorage medium storing processor-executable instructions to:

obtain a user mobile device identifier;

monitor data traffic coming in and out of the user mobile device basedon the obtained user mobile device identifier via a mobile usagetracking entity;

parse the monitored data traffic to determine a data content type of thedata traffic;

determine user media content exposure information from the parsedmonitored data traffic based on the data content type; and

generate user media content exposure statistics data.

42. The medium of embodiment 41, wherein the user mobile deviceidentifier comprises a hardware identifier.

43. The medium of embodiment 41, wherein the user mobile deviceidentifier comprises a physical address.

44. The medium of embodiment 41, wherein the data traffic is obtainedvia a mobile application instantiated on the user mobile device.

45. The medium of embodiment 41, wherein the monitoring data traffic isobtained via a mobile meter.

46. The medium of embodiment 41, wherein the determine a data contenttype of the data traffic comprises extracting a data type filed valuefrom a data event message.

47. The medium of embodiment 41, wherein the data content type comprisesany of:

a URL link, application information, media usage data, survey responsedata, and social data.

48. The medium of embodiment 41, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises a URL link; and

determining the URL link includes an advertisement.

49. The medium of embodiment 28, wherein the determining user mediacontent exposure information further comprises:

determining a classification of the advertisement;

obtaining identifying information of the advertisement; and

storing the identifying information with the user as advertisementexposure information.

50. The medium of embodiment 41, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises applicationinformation;

determining device application inventory; and

obtaining application group sharing information

51. The medium of embodiment 30, wherein the application group sharinginformation comprises a list of social connections.

52. The medium of embodiment 41, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises media usageinformation; and

determining a media title.

53. The medium of embodiment 41, wherein the determining user mediacontent exposure information comprises:

determining the monitored data traffic comprises social content; and

processing the social content to extract user impression indication.

54. The medium of embodiment 33, wherein the user impression indicationis related to a product name.

55. The medium of embodiment 41, wherein the data traffic is monitoredat a proxy server.

56. The medium of embodiment 41, wherein the monitored data trafficfurther comprises any of:

TV channel changing events, mobile advertising data, mobile applicationusage data, social media profile, social media comments, and websitevisits.

57. The medium of embodiment 41, wherein the user media content exposurestatistics data comprises any of user brand impression measures.

58. The medium of embodiment 57, wherein the user brand impressionmeasures comprises a list of top mentioned brands.

59. The medium of embodiment 41, further comprising:

providing and generating individualized ad contents to the user mobiledevice based on the generated user media content exposure statisticsdata.

60. The medium of embodiment 41, further comprising:

generating individualized survey questions to the user mobile devicebased on the generated user media content exposure statistics data.

In order to address various issues and advance the art, the entirety ofthis application for AUDIENCE ATMOSPHERICS MONITORING PLATFORM METHODS(including the Cover Page, Title, Headings, Field, Background, Summary,Brief Description of the Drawings, Detailed Description, Embodiments,Abstract, Figures, Appendices, and otherwise) shows, by way ofillustration, various embodiments in which the claimed innovations maybe practiced. The advantages and features of the application are of arepresentative sample of embodiments only, and are not exhaustive and/orexclusive. They are presented only to assist in understanding and teachthe claimed principles. It should be understood that they are notrepresentative of all claimed innovations. As such, certain aspects ofthe disclosure have not been discussed herein. That alternateembodiments may not have been presented for a specific portion of theinnovations or that further undescribed alternate embodiments may beavailable for a portion is not to be considered a disclaimer of thosealternate embodiments. It may be appreciated that many of thoseundescribed embodiments incorporate the same principles of theinnovations and others are equivalent. Thus, it is to be understood thatother embodiments may be utilized and functional, logical, operational,organizational, structural and/or topological modifications may be madewithout departing from the scope and/or spirit of the disclosure. Assuch, all examples and/or embodiments are deemed to be non-limitingthroughout this disclosure. Also, no inference should be drawn regardingthose embodiments discussed herein relative to those not discussedherein other than it is as such for purposes of reducing space andrepetition. For instance, it is to be understood that the logical and/ortopological structure of any combination of any program components (acomponent collection), other components and/or any present feature setsas described in the figures and/or throughout are not limited to a fixedoperating order and/or arrangement, but rather, any disclosed order isexemplary and all equivalents, regardless of order, are contemplated bythe disclosure. Furthermore, it is to be understood that such featuresare not limited to serial execution, but rather, any number of threads,processes, services, servers, and/or the like that may executeasynchronously, concurrently, in parallel, simultaneously,synchronously, and/or the like are contemplated by the disclosure. Assuch, some of these features may be mutually contradictory, in that theycannot be simultaneously present in a single embodiment. Similarly, somefeatures are applicable to one aspect of the innovations, andinapplicable to others. In addition, the disclosure includes otherinnovations not presently claimed. Applicant reserves all rights inthose presently unclaimed innovations including the right to embodimentsuch innovations, file additional applications, continuations,continuations in part, divisions, and/or the like thereof. As such, itshould be understood that advantages, embodiments, examples, functional,features, logical, operational, organizational, structural, topological,and/or other aspects of the disclosure are not to be consideredlimitations on the disclosure as defined by the embodiments orlimitations on equivalents to the embodiments. It is to be understoodthat, depending on the particular needs and/or characteristics of aATMOS individual and/or enterprise user, database configuration and/orrelational model, data type, data transmission and/or network framework,syntax structure, and/or the like, various embodiments of the ATMOS, maybe implemented that facilitates a great deal of flexibility andcustomization. While various embodiments and discussions of the ATMOShave been directed to social networks, however, it is to be understoodthat the embodiments described herein may be readily configured and/orcustomized for a wide variety of other applications and/orimplementations.

1. A TV audience monitoring processor-implemented method, comprising:receiving, from a user mobile device, an atmospherics data packageindicating user instant activity status; obtaining an atmospherics dataartifact from the atmospherics data package; extracting user instantactivities information based on analysis of the atmospherics dataartifact; generating a user viewing status indication based on the userinstant activities information; and incorporating the user viewingstatus indication into viewer measurement data of a user selectedchannel.
 2. The method of claim 1, wherein the user mobile devicecomprises any of a smartphone, a personal data assistant, a cellularphone, a laptop, a tablet computer and a standalone table unit device.3. The method of claim 1, wherein the TV program schedule listing datais transmitted via a wireless network.
 4. The method of claim 1, furthercomprising: receiving a user channel selection from the user mobiledevice.
 5. The method of claim 1, wherein the atmospherics data packageis captured and aggregated by the user mobile device to monitor what theuser is watching.
 6. The method of claim 1, further comprisingdetermining a type of the atmospherics data artifact.
 7. The method ofclaim 1, wherein the atmospherics data artifact comprises an audio file.8. The method of claim 7, further comprising: determining audio contentbased on audio analysis.
 9. The method of claim 8, further comprising:when the audio content includes human voice, performing voicerecognition to determine whether a vocal source matches a character in aTV show scheduled on the user selected channel;
 10. The method of claim8, further comprising: when the audio content includes human voice,extracting key terms from the human voice to determine whether the vocalcontent is related to a TV show scheduled on the user selected channel.11. The method of 8, further comprising: when the audio content includesambient noise, determining an audience environment status based on thenoise level.
 12. The method of claim 8, further comprising: when theaudio content includes media music, determine whether the media music isrelated to a TV show scheduled on the user selected channel.
 13. Themethod of claim 1, wherein the atmospherics data artifact comprises animage file.
 14. The method of claim 1, further comprising: determininggraphic content based on image analysis.
 15. The method of claim 14,further comprising: when the graphic content comprises audiencepresence, performing facial recognition to determine a number ofpresented audiences.
 16. The method of claim 15, further comprising:when the graphic content comprises a TV screen, determining whether theTV screen is related to a TV show scheduled on the user selectedchannel.
 17. The method of claim 1, wherein the atmospherics dataartifact comprises GPS information.
 18. The method of claim 1, furthercomprising: determining an address type of the GPS information; anddetermining whether the user is viewing TV based on the address type.19. The method of claim 1, further comprising: identifying a mediaprogram based on the received atmospherics data package; and associatingthe identified media program with the viewer measurement data.
 20. Themethod of claim 19, wherein the media program identification isperformed without user input of channel selection.